Review of Automatic Feature Extraction from High-Resolution … · 2018-12-08 · trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy

remote sensing

Review

Review of Automatic Feature Extraction fromHigh-Resolution Optical Sensor Data for UAV-BasedCadastral Mapping

Sophie Crommelinck Rohan Bennett Markus Gerke Francesco Nex Michael Ying Yangand George Vosselman

Faculty of Geo-Information Science and Earth Observation (ITC) University of Twente Enschede 7500 AEThe Netherlands rmbennettutwentenl (RB) mgerkeutwentenl (MG) fnexutwentenl (FN)michaelyangutwentenl (MYY) georgevosselmanutwentenl (GV) Correspondence scrommelinckutwentenl Tel +31-53-489-5524

Academic Editors Farid Melgani Gonzalo Pajares Martinsanz Richard Muumlller and Prasad S ThenkabailReceived 30 June 2016 Accepted 11 August 2016 Published 22 August 2016

Abstract Unmanned Aerial Vehicles (UAVs) have emerged as a rapid low-cost and flexibleacquisition system that appears feasible for application in cadastral mapping high-resolutionimagery acquired using UAVs enables a new approach for defining property boundaries HoweverUAV-derived data are arguably not exploited to its full potential based on UAV data cadastralboundaries are visually detected and manually digitized A workflow that automatically extractsboundary features from UAV data could increase the pace of current mapping proceduresThis review introduces a workflow considered applicable for automated boundary delineationfrom UAV data This is done by reviewing approaches for feature extraction from variousapplication fields and synthesizing these into a hypothetical generalized cadastral workflowThe workflow consists of preprocessing image segmentation line extraction contour generationand postprocessing The review lists example methods per workflow stepmdashincluding a descriptiontrialed implementation and a list of case studies applying individual methods Furthermore accuracyassessment methods are outlined Advantages and drawbacks of each approach are discussed interms of their applicability on UAV data This review can serve as a basis for future work on theimplementation of most suitable methods in a UAV-based cadastral mapping workflow

Keywords UAV Photogrammetry optical sensors HRSI image segmentation line extractioncontour generation image analysis OBIA land administration cadastral boundaries

1 Introduction

Unmanned Aerial Vehicles (UAVs) have emerged as rapid efficient low-cost and flexibleacquisition systems for remote sensing data [1] The data acquired can be of high-resolutionand accuracy ranging from a sub-meter level to a few centimes [23] A photogrammetric UAVworkflow includes flight planning image acquisition mostly camera calibration image orientationand data processing which can result in Digital Surface Models (DSMs) orthoimages and pointclouds [4] UAVs are described as a capable sourcing tool for remote sensing data since theyallow flexible maneuverings high-resolution image capture flying under clouds easy launch andlanding and fast data acquisition at low cost Disadvantages include payload limitations uncertain orrestricting airspace regulations battery induced short flight duration and time consuming processingof large volumes of data gathered [56] In addition multiple factors that influence the accuracyof derived products require extensive consideration This includes the quality of the camera thecamera calibration the number and location of ground control points and the choice of processingsoftware [25] UAVs have been employed in a variety of applications such as the documentation

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Remote Sens 2016 8 689 2 of 28

of archaeological sites and cultural heritage [78] vegetation monitoring in favor of precisionagriculture [910] traffic monitoring [11] disaster management [1213] and 3D reconstruction [14]

Another emerging application field is UAV-based cadastral mapping Cadastral maps arespatial representations of cadastre records showing the extent value and ownership of land [15]Cadastral maps are intended to provide a precise description and identification of land parcels whichare crucial for a continuous and sustainable recording of land rights [16] Furthermore cadastralmaps support land and property taxation allow the development and monitoring of land marketssupport urban planning infrastructure development and allow for producing statistical data Extensivereviews on concepts and purposes of cadastres in relation to land administration are given in [1718]UAVs are proposed as a new tool for fast and cheap spatial data production that enable the productionof cadastral maps Within this field UAVs facilitate land administration processes and contribute tosecuring land tenure [19] UAVs enable a new approach to the establishment and updating of cadastralmaps that contribute to new concepts in land administrations such as fit-for-purpose [20] pro-poorland administration [21] and responsible land administration [22]

11 Application of UAV-Based Cadastral Mapping

In the context of contemporary cadastral mapping UAVs are increasingly argued anddemonstrated as tools able to generate accurate and georeferenced high-resolution imagerymdashfromwhich cadastral boundaries can be visually detected and manually digitized [23ndash25] In order tosupport this manual digitization existing parcel boundary lines might be automatically superimposedwhich could facilitate and accelerate cadastral mapping [26] With the exception of [127] cadastralmapping is not mentioned in review papers on application fields of UAVs [28ndash30] This might be dueto the small amount of case studies within this field the often highly prescribed legal regulationsrelating to cadastral surveys and the novelty of UAV in mapping generally Nevertheless all existingcase studies underline the high potential of UAVs for cadastral mappingmdashin both urban and ruralcontexts for developing and developed countries

In developing countries cadastral mapping contributes to the creation of formal systems forregistering and safeguarding land rights According to the World Bank and the International Federationof Surveyors (FIG) 75 of the worldrsquos population do not have access to such systems Further theystate that 90 countries lack land registration systems while 50 countries are in the process of establishingsuch systems [20] In these countries cadastral mapping is often based on partly outdated maps orsatellite images of low-resolution which might include areas covered by clouds Numerous studieshave investigated cadastral mapping based on orthoimages derived from satellite imagery [2231ndash37] oraerial photography [38] The definition of boundary lines is often conducted in a collaborative processamong members of the communities governments and aid organizations which is referred to asldquoCommunity Mappingrdquo [39] ldquoParticipatory Mappingrdquo [22] or ldquoParticipatory GISrdquo [31] Such outdatedsatellite images are substitutable through up-to-date high-resolution orthoimages derived from UAVsas shown in case studies in Namibia [24] and Rwanda [23] The latter case shows the utility of UAVs topartially update existing cadastral maps

In developed countries the case studies focus on the conformity of the UAV datarsquosaccuracy with local accuracy standards and requirements [4041] Furthermore case studies tendto investigate possibilities of applying UAVs to reshape the cadastral production line efficiency andeffectiveness [42ndash44] In the latter manual boundary detection with all stakeholders is conducted inan office eliminating the need for convening all stakeholders on the parcel In developed countriesUAV data are frequently used to update small portions of existing cadastral maps rather than creatingnew ones Airspace regulations are the most limiting factor that hinder the thorough use of UAVsCurrently regulatory bodies face the alignment of economic information and safety needs or demandsconnected to UAVs [3045] Once these limitations are better aligned with societal needs UAVsmight be employed for further fields of land administration including the monitoring of publicinfrastructure like oil and gas pipelines power lines dikes highways and railways [46] Nowadays

some national mapping agencies in Europe integrate but mainly investigate the use of UAVs forcadastral mapping [45]

Overall UAVs are employed to support land administration both in creating and updatingcadastral maps The entirety of case studies confirms that UAVs are suitable as an addition toconventional data acquisition methods in order to create detailed cadastral maps including overviewimages or 3D models [404147] The average geometrical precision is shown to be the same or bettercompared to conventional terrestrial surveying methods [42] UAVs will not substitute conventionalapproaches since they are currently not suited to map large areas such as entire countries [48]The employment of UAVs supports the economic feasibility of land administration and contributes tothe accuracy and completeness of cadastral maps

12 Boundary Delineation for UAV-Based Cadastral Mapping

In all case studies cadastral boundaries are manually detected and digitized from orthoimagesThis is realized either in an office with a small group of involved stakeholdersmdashfor one parcel orin a community mapping approach for several parcels at once All case studies lack an automaticapproach to extract boundary features from the UAV data An automatic or semi-automatic featureextraction process would facilitate cadastral mapping manual feature extraction is generally regardedas time-consuming wherefore an automation will bring substantial benefits [4] The degree ofautomation can range from semi-automatic including human interaction to fully automatic Due to thecomplexity of image understanding fully automatic feature extraction often shows a certain error rateTherefore human interaction can hardly be excluded completely [49] However even a semi-automaticor partial extraction of boundary features would alter cadastral mapping with regards to cost and timeJazayeri et al state that UAV data have the potential for automated object reconstruction and boundaryextraction activities to be accurate and low-cost [50] This is especially true for visible boundariesmanifested physically by objects such as hedges stone walls large scale monuments walkwaysditches or fences which often coincide with cadastral boundaries [5152] Such visible boundaries bearthe potential to be automatically extracted from UAV data To the best of the authorsrsquo knowledge noresearch has been done on expediting the cadastral mapping workflow through automatic boundarydelineation from UAV data

13 Objective and Organization of This Paper

The review is based on the assumption that image processing algorithms applied tohigh-resolution UAV data are applicable to determine cadastral boundaries Therefore methodsare reviewed that are deemed feasible for detecting and delineating cadastral boundaries The reviewis intended to serve as a basis for future work on the implementation of the most suitable methodsin a UAV-based cadastral mapping workflow The degree of automation of the final workflow is leftundetermined at this point Due to an absence of work in this context the scope of this review isextended to methods that could be used for UAV-based cadastral mapping but that are currentlyapplied (i) on different data sources or (ii) for different purposes

(i) UAV data includes dense point clouds from which DSMs are derived as well as high-resolutionimagery Such products can be similarly derived from other high-resolution optical sensorsTherefore methods based on other high-resolution optical sensor data such as High-ResolutionSatellite Imagery (HRSI) and aerial imagery are equally considered in this review Methods appliedsolely on 3D point clouds are excluded Methods that are based on the derived DSM are consideredin this review Methods that combine 3D point clouds and aerial or satellite imagery are consideredin terms of methods based on the aerial or satellite imagery

(ii) The review includes methods that aim to extract features other than cadastral boundaries havingsimilar characteristics which are outlined in the next section Suitable methods are not intended toextract the entirety of boundary features since some boundaries are not visible to optical sensors

This paper is structured as follows Firstly the objects to be automatically extracted are defined anddescribed Therefore cadastral boundary concepts and common cadastral boundary characteristics areoutlined Secondly methods that are feasible to automatically detect and extract previously outlinedboundary features are listed The methods are structured according to subsequently applicableworkflow steps Thereafter representative methods are applied on an example UAV dataset tovisualize their performance and applicability on UAV data Thirdly accuracy assessment methodsare outlined Finally the methods are discussed in terms of the advantages and drawbacks faced incase studies and during the implementation of representative methods The term ldquocase studiesrdquo isextended to studies on method development followed by examples in this review The conclusioncovers recommendations on suitable approaches for boundary delineation and issues to address infuture work

2 Review of Feature Extraction and Evaluation Methods

21 Cadastral Boundary Characteristics

In this paper a cadastral boundary is defined as a dividing entity with a spatial reference thatseparates adjacent land plots An overview on concepts and understandings of boundaries in differentdisciplines is given in [52] Cadastral boundaries can be represented in two different ways (i) In manycases they are represented as line features that clearly demarcate the boundaryrsquos spatial position(ii) Some approaches employ laminar features that represent a cadastral area without clear boundariesThe cadastral boundary is then defined implicitly based on the outline or center of the area constitutingthe boundary [53] This is beneficial for ecotones that represent transitional zones between adjacentecosystems or for pastoralists that move along areas In such cases cadastral boundaries seek tohandle overlapping access rights and to grant spatiotemporal mobility [54ndash56] As shown a cadastralboundary does not merely include spatial aspects but those of time and scale as well [5758]

Different approaches exist to categorize concepts of cadastral boundaries The lines between thedifferent categories presented in the following can be understood as fuzzy They are drawn to givea general overview visualized in Figure 1 From a technical point of view cadastral boundaries aredividable into two categories (i) Fixed boundaries whose accurate spatial position has been recordedand agreed upon and (ii) general boundaries whose precise spatial position is left undetermined [59]Both require surveying and documentation in cadastral mapping Cadastral surveying techniques canbe distinguished between (i) direct techniques in which the accurate spatial position of a boundary ismeasured on the ground using theodolite total stations and Global Navigation Satellite System (GNSS)and (ii) indirect techniques in which remotely sensed data such as aerial or satellite imagery are appliedThe spatial position of boundaries is derived from these data in a second step [32] Fixed boundariesare commonly measured with direct techniques which provide the required higher accuracy Indirecttechniques including UAVs are able to determine fixed boundaries only in case of high-resolutiondata Indirect techniques are mostly applied to extract visible boundaries These are determined byphysical objects and coincide with the concept of general boundaries [5152] This review concentrateson methods that delineate general ie visible cadastral boundaries from indirect surveying techniquesof high-resolution The methods are intended to automatically extract boundary features and to beapplicable to UAV data

In order to understand which visible boundaries define the extents of land literature on 2Dcadastral mappingmdashbased on indirect techniquesmdashwas reviewed to identify common boundarycharacteristics Man-made objects are found to define cadastral boundaries as well as natural objectsStudies name buildings hedges fences walls roads footpaths pavement open areas crop typeshrubs rivers canals and water drainages as cadastral boundary features [243132344260ndash62]Trees are named as the most limiting factor since they often obscure the view of the actualboundary [4163] No study summarizes characteristics of detected cadastral boundaries eventhough it is described as crucial for feature recognition to establish a model that describes thegeneral characteristics of the feature of interest [64] Common in many approaches is the linearityof extracted features This might be due to the fact that some countries do not accept curved

cadastral boundaries [33] Even if a curved river marks the cadastral boundary the boundary line isapproximated by a polygon [32] When considering the named features the following characteristicscan be derived most features have a continuous and regular geometry expressed in long straightlines of a limited curvature Furthermore features often share common spectral properties such assimilar values in color and texture Moreover boundary features are topologically connected and forma network of lines that surround land parcels of a certain (minimal) size and shape Finally boundariesmight be indicated by a special distribution of other objects such as trees In summary features aredetectable based on their geometry spectral property topology and context

continuous and regular geometry expressed in long straight lines of a limited curvature Furthermore features often share common spectral properties such as similar values in color and texture Moreover boundary features are topologically connected and form a network of lines that surround land parcels of a certain (minimal) size and shape Finally boundaries might be indicated by a special distribution of other objects such as trees In summary features are detectable based on their geometry spectral property topology and context

Figure 1 Overview of cadastral surveying techniques and cadastral boundary concepts that contextualize the scope of this review paper The lines between different categories are fuzzy and should not be understood exclusively They are drawn to give a general overview

This review focuses on methods that extract linear boundary features since cadastral boundaries are commonly represented by straight lines with exceptions outlined in [5565] Cadastral representations in 3D as described in [66] are excluded With the employment of UAVs not all cadastral boundaries can be detectable Only those detectable with an optical sensor ie visible boundaries can be extracted This approach does not consider non-visible boundaries that are not marked by a physical object These might be socially perceived boundaries or arbitrary boundaries originating from a continuous subdivision of land parcels Figure 2 provides an overview of visible boundary characteristics mentioned before and commonly raised issues in terms of their detection

Figure 2 Characteristics of cadastral boundaries extracted from high-resolution optical remote sensors The cadastral boundaries are derived based on (a) roads fences and edges of agricultural fields [48] (b) fences and hedges [24] (cd) crop types [41] (e) adjacent vegetation [63] and (f) roads foot paths water drainage open areas and scrubs [67] (d) Shows the case of a nonlinear irregular boundary shape The cadastral boundaries in (e) and (f) are often obscured by tree canopy Cadastral

Figure 1 Overview of cadastral surveying techniques and cadastral boundary concepts thatcontextualize the scope of this review paper The lines between different categories are fuzzy andshould not be understood exclusively They are drawn to give a general overview

This review focuses on methods that extract linear boundary features since cadastralboundaries are commonly represented by straight lines with exceptions outlined in [5565]Cadastral representations in 3D as described in [66] are excluded With the employment of UAVs notall cadastral boundaries can be detectable Only those detectable with an optical sensor ie visibleboundaries can be extracted This approach does not consider non-visible boundaries that are notmarked by a physical object These might be socially perceived boundaries or arbitrary boundariesoriginating from a continuous subdivision of land parcels Figure 2 provides an overview of visibleboundary characteristics mentioned before and commonly raised issues in terms of their detection

Figure 2 Characteristics of cadastral boundaries extracted from high-resolution optical remote sensorsThe cadastral boundaries are derived based on (a) roads fences and edges of agricultural fields [48](b) fences and hedges [24] (cd) crop types [41] (e) adjacent vegetation [63] and (f) roads foot pathswater drainage open areas and scrubs [67] (d) Shows the case of a nonlinear irregular boundary shapeThe cadastral boundaries in (e) and (f) are often obscured by tree canopy Cadastral boundaries in (andashd)are derived from UAV data in (e) and (f) from HRSI All of the boundaries are manually extractedand digitized

22 Feature Extraction Methods

This section reviews methods that are able to detect and extract the above mentioned boundarycharacteristics The methods reviewed are either pixel-based or object-based (i) Pixel-based approachesanalyze single pixels optionally taking into account the pixelsrsquo context which can be consideredthrough moving windows or implicitly through modeling These data-driven approaches areoften employed when the object of interest is smaller or similar in size as the spatial resolutionExample exceptions are modern convolutional neural networks (CNN) [68] which are explainedin the latter The lack of an explicit object topology is one drawback that might lead to inferiorresults in particular for topographic mapping applications compared to those of human vision [69](ii) Object-based approaches are employed to explicitly integrate knowledge of object appearance andtopology into the object extraction process Applying these approaches becomes possible once thespatial resolution is finer than the object of interest In such cases pixels with similar characteristics suchas color tone texture shape context shadow or semantics are grouped to objects Such approachesare referred to as Object Based Image Analysis (OBIA) They are considered model-driven sinceknowledge about scene understanding is incorporated to structure the image content spatially andsemantically The grouping of pixels might also results into groups of pixels called superpixelsThis approach with corresponding methods explained in Section 222 could be seen as a thirdin-between category but is understood as object-based in this review [70ndash72]

Pixel-based approaches are often used to extract low-level features which do not considerinformation about spatial relationships Low-level features are extracted directly from the raw possiblynoisy pixels with edge detection being the most prominent algorithms [73] Object-based approachesare used to extract high-level features which represent shapes in images that are detected invariant ofillumination translation orientation and scale High-level features are mostly extracted based on theinformation provided by low-level features [73] High-level feature extraction aimed at automatedobject detection and extraction is currently achieved in a stepwise manner and is still an active researchfield [74] Algorithms for high-level feature extraction often need to be interlinked to a processingworkflow and do not lead to appropriate results when applied solely [70] The relation of the describedconcepts is visualized in Figure 3 Both pixel-based and object-based approaches are applicable toUAV data Pixel-based approaches can be applied to UAV data or to its down sampled version oflower resolution Due to the possible high Ground Sample Distance (GSD) of 1ndash5 cm [6] of UAV dataobject-based approaches seem to be preferred Both approaches are included in this review as theability to discriminate and extract features is highly dependent on scale [7576]

boundaries in (andashd) are derived from UAV data in (e) and (f) from HRSI All of the boundaries are manually extracted and digitized

This section reviews methods that are able to detect and extract the above mentioned boundary characteristics The methods reviewed are either pixel-based or object-based (i) Pixel-based approaches analyze single pixels optionally taking into account the pixelsrsquo context which can be considered through moving windows or implicitly through modeling These data-driven approaches are often employed when the object of interest is smaller or similar in size as the spatial resolution Example exceptions are modern convolutional neural networks (CNN) [68] which are explained in the latter The lack of an explicit object topology is one drawback that might lead to inferior results in particular for topographic mapping applications compared to those of human vision [69] (ii) Object-based approaches are employed to explicitly integrate knowledge of object appearance and topology into the object extraction process Applying these approaches becomes possible once the spatial resolution is finer than the object of interest In such cases pixels with similar characteristics such as color tone texture shape context shadow or semantics are grouped to objects Such approaches are referred to as Object Based Image Analysis (OBIA) They are considered model-driven since knowledge about scene understanding is incorporated to structure the image content spatially and semantically The grouping of pixels might also results into groups of pixels called superpixels This approach with corresponding methods explained in Section 222 could be seen as a third in-between category but is understood as object-based in this review [70ndash72]

Pixel-based approaches are often used to extract low-level features which do not consider information about spatial relationships Low-level features are extracted directly from the raw possibly noisy pixels with edge detection being the most prominent algorithms [73] Object-based approaches are used to extract high-level features which represent shapes in images that are detected invariant of illumination translation orientation and scale High-level features are mostly extracted based on the information provided by low-level features [73] High-level feature extraction aimed at automated object detection and extraction is currently achieved in a stepwise manner and is still an active research field [74] Algorithms for high-level feature extraction often need to be interlinked to a processing workflow and do not lead to appropriate results when applied solely [70] The relation of the described concepts is visualized in Figure 3 Both pixel-based and object-based approaches are applicable to UAV data Pixel-based approaches can be applied to UAV data or to its down sampled version of lower resolution Due to the possible high Ground Sample Distance (GSD) of 1ndash5 cm [6] of UAV data object-based approaches seem to be preferred Both approaches are included in this review as the ability to discriminate and extract features is highly dependent on scale [7576]

Figure 3 Pixel-based and object-based feature extraction approaches aim to derive low-level and high-level features from images Object-based approaches may include information provided by low-level features that is used for high-level feature extraction

The reviewed methods are structured according to a sequence of commonly applied workflow steps for boundary delineation as shown in Figure 4 The structure of first identifying candidate

Figure 3 Pixel-based and object-based feature extraction approaches aim to derive low-level andhigh-level features from images Object-based approaches may include information provided bylow-level features that is used for high-level feature extraction

The reviewed methods are structured according to a sequence of commonly applied workflowsteps for boundary delineation as shown in Figure 4 The structure of first identifying candidate

regions then detecting linear features and finally connecting these appears to be a generic approach asfollowing literature exemplifies A review on linear feature extraction from imagery [64] a review onroad detection [77] and case studies that aim to extract road networks from aerial imagery [7879] andto delineate tree outlines from HRSI [80] The first step image segmentation aims to divide an imageinto non-overlapping segments in order to identify candidate regions for further processing [81ndash83]The second step line extraction detects edges Edges are defined as a step change in the value ofa low-level feature such as brightness or color A collinear collection of such edges aggregated on thebasis of grouping criteria is commonly defined as a line [84ndash86] The third step contour generationconnects lines to form a closed vectorized boundary line that surrounds an area defined throughsegmentation These main steps can optionally be extended with pre- and postprocessing steps

regions then detecting linear features and finally connecting these appears to be a generic approach as following literature exemplifies A review on linear feature extraction from imagery [64] a review on road detection [77] and case studies that aim to extract road networks from aerial imagery [7879] and to delineate tree outlines from HRSI [80] The first step image segmentation aims to divide an image into non-overlapping segments in order to identify candidate regions for further processing [81ndash83] The second step line extraction detects edges Edges are defined as a step change in the value of a low-level feature such as brightness or color A collinear collection of such edges aggregated on the basis of grouping criteria is commonly defined as a line [84ndash86] The third step contour generation connects lines to form a closed vectorized boundary line that surrounds an area defined through segmentation These main steps can optionally be extended with pre- and postprocessing steps

Figure 4 Sequence of commonly applied workflow steps to detect and extract linear features used to structure the methods reviewed

This review includes 37 case studies of unknown resolution and 52 case studies of multiple resolutions most often below 5 m (Figure 5) The investigated case studies intend to detect features such as coastlines agricultural field boundaries road networks and buildings from aerial or satellite imagery which is mainly collected with IKONOS or QuickBird satellites A minority of studies is based on UAV data The methods are often equally applicable on aerial and satellite imagery as the data sources can have similar characteristics such as the high resolution of the derived orthoimages [87]

Figure 5 Spatial resolution of data used in the case studies The figure shows the 52 case studies in which the spatial resolution was known For case studies that use datasets of multiple resolutions the median resolution is used For 37 further case studies which are not represented in the histogram the spatial resolution was left undetermined

In the following each workflow step is explained in detail including a table of example methods and case studies that apply these methods The table represents possible approaches with various further methods possible The most common strategies are covered while specific adaptations derived from these are excluded to limit the extent of this survey Overall the survey of methods in

Figure 4 Sequence of commonly applied workflow steps to detect and extract linear features used tostructure the methods reviewed

This review includes 37 case studies of unknown resolution and 52 case studies of multipleresolutions most often below 5 m (Figure 5) The investigated case studies intend to detect featuressuch as coastlines agricultural field boundaries road networks and buildings from aerial or satelliteimagery which is mainly collected with IKONOS or QuickBird satellites A minority of studies is basedon UAV data The methods are often equally applicable on aerial and satellite imagery as the datasources can have similar characteristics such as the high resolution of the derived orthoimages [87]

Figure 5 Spatial resolution of data used in the case studies The figure shows the 52 case studies inwhich the spatial resolution was known For case studies that use datasets of multiple resolutions themedian resolution is used For 37 further case studies which are not represented in the histogram thespatial resolution was left undetermined

In the following each workflow step is explained in detail including a table of example methodsand case studies that apply these methods The table represents possible approaches with various

further methods possible The most common strategies are covered while specific adaptations derivedfrom these are excluded to limit the extent of this survey Overall the survey of methods in thisreview is extensive but it does not claim to be complete The description and contextualizationof most methods is based upon [88ndash91] Due to the small amount of case studies on linear featureextraction that employ high resolution sensors of lt05 m one group of the described table includes casestudies on resolutions of up to 5 m whereas the other includes the remaining case studies In orderto demonstrate the applicability of the methods on UAV imagery for boundary delineation somerepresentative methods were implemented An orthoimage acquired with a fixed-wing UAV duringa flight campaign in Namibia served as an exemplary dataset (Figure 6) It shows a rural residentialhousing area and has a GSD of 5 cm The acquisition and processing of the images is described in [24]Cadastral boundaries are marked with fences and run along paths in this exemplary dataset In urbanareas the cadastral parcels might be marked differently ie through roof outlines The proposedworkflow would be similarly applicable in such areas possibly detecting a larger number of cadastralboundaries due a consistency in cadastral boundary objects and smaller parcels However a largenumber of cadastral boundaries might not be visible in urban areas eg when running throughbuildings with the same roof Therefore a rural area is considered as exemplary dataset

As for the implementation image processing libraries written in Python and Matlab wereconsidered For Python this included Scikit [92] and OpenCV modules [93] The latter are equallyavailable in C++ For Matlab example code provided from MathWorks [94] and VLFeat [95]was adopted The methods were implemented with different libraries and standard parametersThe visually most representative output was chosen for this review as an illustrative explanation ofdiscussed methods

this review is extensive but it does not claim to be complete The description and contextualization of most methods is based upon [88ndash91] Due to the small amount of case studies on linear feature extraction that employ high resolution sensors of lt05 m one group of the described table includes case studies on resolutions of up to 5 m whereas the other includes the remaining case studies In order to demonstrate the applicability of the methods on UAV imagery for boundary delineation some representative methods were implemented An orthoimage acquired with a fixed-wing UAV during a flight campaign in Namibia served as an exemplary dataset (Figure 6) It shows a rural residential housing area and has a GSD of 5 cm The acquisition and processing of the images is described in [24] Cadastral boundaries are marked with fences and run along paths in this exemplary dataset In urban areas the cadastral parcels might be marked differently ie through roof outlines The proposed workflow would be similarly applicable in such areas possibly detecting a larger number of cadastral boundaries due a consistency in cadastral boundary objects and smaller parcels However a large number of cadastral boundaries might not be visible in urban areas eg when running through buildings with the same roof Therefore a rural area is considered as exemplary dataset

As for the implementation image processing libraries written in Python and Matlab were considered For Python this included Scikit [92] and OpenCV modules [93] The latter are equally available in C++ For Matlab example code provided from MathWorks [94] and VLFeat [95] was adopted The methods were implemented with different libraries and standard parameters The visually most representative output was chosen for this review as an illustrative explanation of discussed methods

Figure 6 UAV-derived orthoimage that shows a rural residential housing area in Namibia which is used as an exemplary dataset to implement representative feature extraction methods

221 Preprocessing

Preprocessing steps might be applied in order to improve the output of the subsequent image segmentation and to facilitate the extraction of linear features Therefore the image is processed to suppress noise and enhance image details The preprocessing includes the adjustment of contrast and brightness and the application of smoothing filters to remove noise [96] Two possible approaches that aim at noise removal and image enhancement are presented in the following Further approaches can be found in [97]

Anisotropic diffusion aims at reducing image noise while preserving significant parts of the image content (Figure 7 based on source code provided in [98]) This is done in an iterative process of applying an image filter until a sufficient degree of smoothing is obtained [9899]

Wallis filter is an image filter method for detail enhancement through local contrast adjustment The algorithm subdivides an image into non-overlapping windows of the same size to then adjust the contrast and minimize radiometric changes of each window [100]

Figure 6 UAV-derived orthoimage that shows a rural residential housing area in Namibia which isused as an exemplary dataset to implement representative feature extraction methods

221 Preprocessing

Preprocessing steps might be applied in order to improve the output of the subsequent imagesegmentation and to facilitate the extraction of linear features Therefore the image is processed tosuppress noise and enhance image details The preprocessing includes the adjustment of contrast andbrightness and the application of smoothing filters to remove noise [96] Two possible approaches thataim at noise removal and image enhancement are presented in the following Further approaches canbe found in [97]

Anisotropic diffusion aims at reducing image noise while preserving significant parts of theimage content (Figure 7 based on source code provided in [98]) This is done in an iterativeprocess of applying an image filter until a sufficient degree of smoothing is obtained [9899]

Wallis filter is an image filter method for detail enhancement through local contrast adjustmentThe algorithm subdivides an image into non-overlapping windows of the same size to then adjustthe contrast and minimize radiometric changes of each window [100]

(a) (b)

Figure 7 (a) Subset of the original UAV orthoimage converted to greyscale (b) Anisotropic diffusion applied on greyscale UAV image to reduce noise After filtering the image appears smoothed with sharp contours removed which can be observed at the rooftops and tree contours

222 Image Segmentation

This section describes methods that divide an image into non-overlapping segments that represent areas The segments are detected based on homogeneity parameters or on the differentiation to neighboring regions [101] In a non-ideal case the image segmentation creates segments that cover more than one object of interest or the object of interest is subdivided into several objects These outcomes are referred to as undersegmentation and oversegmentation respectively [101] Various strategies exist to classify image segmentation as shown in [102103] In this review the methods are classified into (i) unsupervised or (ii) supervised approaches Table 1 shows an exemplary selection of case studies that apply the methods described in the following

(i) Unsupervised approaches include methods in which segmentation parameters are defined that describe color texture spectral homogeneity size shape compactness and scale of image segments The challenge lies within defining appropriate segmentation parameters for features varying in size shape scale and spatial location Thereafter the image is automatically segmented according to these parameters [90] Popular approaches are described in the following and visualized in Figure 8 these were often applied in the case studies investigated for this review A list of further approaches can be found in [102]

Graph-based image segmentation is based on color and is able to preserve details in low-variability image regions while ignoring details in high-variability regions The algorithm performs an agglomerative clustering of pixels as nodes on a graph such that each superpixel is the minimum spanning tree of the constituent pixels [104105]

Simple Linear Iterative Clustering (SLIC) is an algorithm that adapts a k-mean clustering approach to generate groups of pixels called superpixels The number of superpixels and their compactness can be adapted within the memory efficient algorithm [106]

Watershed algorithm is an edge-based image segmentation method It is also referred to as a contour filling method and applies a mathematical morphological approach First the algorithm transforms an image into a gradient image The image is seen as a topographical surface where grey values are deemed as elevation of the surface of each pixelrsquos location Then a flooding process starts in which water effuses out of the minimum grey values When the flooding across two minimum values converges a boundary that separates the two identified segments is defined [101102]

Wavelet transform analyses textures and patterns to detect local intensity variations and can be considered as a generalized combination of three other operations Multi-resolution analysis template matching and frequency domain analysis The algorithm decomposes an image into a low frequency approximation image and a set of high frequency spatially oriented detailed images [107]

Figure 7 (a) Subset of the original UAV orthoimage converted to greyscale (b) Anisotropic diffusionapplied on greyscale UAV image to reduce noise After filtering the image appears smoothed withsharp contours removed which can be observed at the rooftops and tree contours

This section describes methods that divide an image into non-overlapping segments that representareas The segments are detected based on homogeneity parameters or on the differentiation toneighboring regions [101] In a non-ideal case the image segmentation creates segments thatcover more than one object of interest or the object of interest is subdivided into several objectsThese outcomes are referred to as undersegmentation and oversegmentation respectively [101]Various strategies exist to classify image segmentation as shown in [102103] In this review themethods are classified into (i) unsupervised or (ii) supervised approaches Table 1 shows an exemplaryselection of case studies that apply the methods described in the following

(i) Unsupervised approaches include methods in which segmentation parameters are defined thatdescribe color texture spectral homogeneity size shape compactness and scale of image segmentsThe challenge lies within defining appropriate segmentation parameters for features varying insize shape scale and spatial location Thereafter the image is automatically segmented accordingto these parameters [90] Popular approaches are described in the following and visualized inFigure 8 these were often applied in the case studies investigated for this review A list of furtherapproaches can be found in [102]

Graph-based image segmentation is based on color and is able to preserve details inlow-variability image regions while ignoring details in high-variability regions The algorithmperforms an agglomerative clustering of pixels as nodes on a graph such that each superpixelis the minimum spanning tree of the constituent pixels [104105]

Simple Linear Iterative Clustering (SLIC) is an algorithm that adapts a k-mean clusteringapproach to generate groups of pixels called superpixels The number of superpixels and theircompactness can be adapted within the memory efficient algorithm [106]

Watershed algorithm is an edge-based image segmentation method It is also referred toas a contour filling method and applies a mathematical morphological approach First thealgorithm transforms an image into a gradient image The image is seen as a topographicalsurface where grey values are deemed as elevation of the surface of each pixelrsquos location Thena flooding process starts in which water effuses out of the minimum grey values When theflooding across two minimum values converges a boundary that separates the two identifiedsegments is defined [101102]

Wavelet transform analyses textures and patterns to detect local intensity variations and can beconsidered as a generalized combination of three other operations Multi-resolution analysistemplate matching and frequency domain analysis The algorithm decomposes an image intoa low frequency approximation image and a set of high frequency spatially oriented detailedimages [107]

Remote Sens 2016 8 689 10 of 28

(ii) Supervised methods often consist of methods from machine learning and pattern recognitionThese can be performed by learning a classifier to capture the variation in object appearances andviews from a training dataset In the training dataset object shape descriptors are defined and usedto label the training dataset Then the classifier is learned based on a set of regions with objectshape descriptors resulting in their corresponding predicted labels The automation of machinelearning approaches might be limited since some classifiers need to be trained with samples thatrequire manual labeling The aim of training is to model the process of data generation such that itcan predict the output for unforeseen data Various possibilities exist to select training sets andfeatures [108] as well as to select a classifier [90109] In contrast to the unsupervised methodsthese methods go beyond image segmentation as they additionally add a semantic meaning to eachsegment A selection of popular approaches that have been applied in case studies investigated forthis review are described in the following A list of further approaches can be found in [90]

Convolutional Neural Networks (CNN) are inspired by biological processes being made upof neurons that have learnable weights and biases The algorithm creates multiple layers ofsmall neuron collections which process parts of an image referred to as receptive fields Thenlocal connections and tied weights are analyzed to aggregate information from each receptivefield [96]

Markov Random Fields (MRF) are a probabilistic approach based on graphical modelsThey are used to extract features based on spatial texture by classifying an image into a numberof regions or classes The image is modelled as a MRF and a maximum a posteriori probabilityapproach is used for classification [110]

Support Vector Machines (SVM) consist of a supervised learning model with associatedlearning algorithms that support linear image classification into two or more categories throughdata modelling Their advantages include a generalization capability which concerns theability to classify shapes that are not within the feature space used for training [111]

Table 1 Case study examples for image segmentation methods

Image Segmentation Method Resolution lt 5 m Resolution gt 5 m Unknown Resolution

Unsupervised [7980112ndash128] [107129ndash133] [125134ndash145]Supervised [7275108146ndash155] [156157] [86137138158ndash160]

Remote Sens 2016 8 689 10 of 27

(ii) Supervised methods often consist of methods from machine learning and pattern recognition These can be performed by learning a classifier to capture the variation in object appearances and views from a training dataset In the training dataset object shape descriptors are defined and used to label the training dataset Then the classifier is learned based on a set of regions with object shape descriptors resulting in their corresponding predicted labels The automation of machine learning approaches might be limited since some classifiers need to be trained with samples that require manual labeling The aim of training is to model the process of data generation such that it can predict the output for unforeseen data Various possibilities exist to select training sets and features [108] as well as to select a classifier [90109] In contrast to the unsupervised methods these methods go beyond image segmentation as they additionally add a semantic meaning to each segment A selection of popular approaches that have been applied in case studies investigated for this review are described in the following A list of further approaches can be found in [90]

Convolutional Neural Networks (CNN) are inspired by biological processes being made up of neurons that have learnable weights and biases The algorithm creates multiple layers of small neuron collections which process parts of an image referred to as receptive fields Then local connections and tied weights are analyzed to aggregate information from each receptive field [96]

Markov Random Fields (MRF) are a probabilistic approach based on graphical models They are used to extract features based on spatial texture by classifying an image into a number of regions or classes The image is modelled as a MRF and a maximum a posteriori probability approach is used for classification [110]

Support Vector Machines (SVM) consist of a supervised learning model with associated learning algorithms that support linear image classification into two or more categories through data modelling Their advantages include a generalization capability which concerns the ability to classify shapes that are not within the feature space used for training [111]

Image Segmentation Method Resolution lt 5 m Resolution gt 5 m Unknown ResolutionUnsupervised [7980112ndash128] [107129ndash133] [125134ndash145]

Supervised [7275108146ndash155] [156157] [86137138158ndash160]

(a) (b) (c)

Figure 8 Image segmentation applied on the original UAV orthoimage (a) Graph-based segmentation (b) SLIC segmentation and (c) Watershed segmentation The label matrices are converted to colors for visualization purpose The input parameters are tuned to obtain a comparable amount of segments from each segmentation approach However all approaches result in differently located and shaped segments

223 Line Extraction

This section describes methods that detect and extract linear features Table 2 shows an exemplary selection of case studies that apply the described methods which are visualized in Figure 9 The figure shows that a large amount of edges is detected especially in the case of vegetation and on the rooftops of buildings while a small amount of edges is detected on paths

Figure 8 Image segmentation applied on the original UAV orthoimage (a) Graph-based segmentation(b) SLIC segmentation and (c) Watershed segmentation The label matrices are converted to colorsfor visualization purpose The input parameters are tuned to obtain a comparable amount ofsegments from each segmentation approach However all approaches result in differently located andshaped segments

223 Line Extraction

This section describes methods that detect and extract linear features Table 2 shows an exemplaryselection of case studies that apply the described methods which are visualized in Figure 9 The figure

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shows that a large amount of edges is detected especially in the case of vegetation and on the rooftopsof buildings while a small amount of edges is detected on paths

Edge detection can be divided into (i) first and (ii) second order derivative based edge detectionAn edge has the one-dimensional shape of a ramp and calculating the derivative of the image canhighlight its location (i) First order derivative based methods detect edges by looking for themaximum and minimum in the first derivative of the image to locate the presence of the highestrate of change between adjacent pixels The most prominent representative is the Canny edgedetection that fulfills the criteria of a good detection and localization quality and the avoidanceof multiple responses These criteria are combined into one optimization criterion and solvedusing the calculus of variations The algorithm consists of Gaussian smoothing gradient filteringnon-maximum suppression and hysteresis thresholding [161] Further representatives based onfirst order derivatives are the Robertrsquos cross Sobel Kirsch and Prewitt operators (ii) Second orderderivative based methods detect edges by searching for zero crossings in the second derivative ofthe image to find edges The most prominent representative is the Laplacian of Gaussian whichhighlights regions of rapid intensity change The algorithm applies a Gaussian smoothing filterfollowed by a derivative operation [162163]

Straight line extraction is mostly done with the Hough transform This is a connected componentanalysis for line circle and ellipse detection in a parameter space referred to as Hough spaceEach candidate object point is transformed into Hough space in order to detect clusters withinthat space that represent the object to be detected The standard Hough transform detectsanalytic curves while a generalized Hough transform can be used to detect arbitrary shapedtemplates [164] As an alternative the Line Segment Detector (LSD) algorithm could be appliedFor this method the gradient orientation that represents the local direction of the intensity valueand the global context of the intensity variations are utilized to group pixels into line-supportregions and to determine the location and properties of edges [84] The method is applied for lineextraction in [165166] The visualization in Figure 9 is based on source code provided in [166]

Table 2 Case study examples for line extraction methods

Line Extraction Method Resolution lt 5 m Resolution gt 5 m Unknown Resolution

Canny edge detection [75121151167] [129168] [138144169170]Hough transform [73120126171] [172] [140169173]

Line segment detector [128165171] [144145174ndash177]

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Edge detection can be divided into (i) first and (ii) second order derivative based edge detection An edge has the one-dimensional shape of a ramp and calculating the derivative of the image can highlight its location (i) First order derivative based methods detect edges by looking for the maximum and minimum in the first derivative of the image to locate the presence of the highest rate of change between adjacent pixels The most prominent representative is the Canny edge detection that fulfills the criteria of a good detection and localization quality and the avoidance of multiple responses These criteria are combined into one optimization criterion and solved using the calculus of variations The algorithm consists of Gaussian smoothing gradient filtering non-maximum suppression and hysteresis thresholding [161] Further representatives based on first order derivatives are the Robertrsquos cross Sobel Kirsch and Prewitt operators (ii) Second order derivative based methods detect edges by searching for zero crossings in the second derivative of the image to find edges The most prominent representative is the Laplacian of Gaussian which highlights regions of rapid intensity change The algorithm applies a Gaussian smoothing filter followed by a derivative operation [162163]

Straight line extraction is mostly done with the Hough transform This is a connected component analysis for line circle and ellipse detection in a parameter space referred to as Hough space Each candidate object point is transformed into Hough space in order to detect clusters within that space that represent the object to be detected The standard Hough transform detects analytic curves while a generalized Hough transform can be used to detect arbitrary shaped templates [164] As an alternative the Line Segment Detector (LSD) algorithm could be applied For this method the gradient orientation that represents the local direction of the intensity value and the global context of the intensity variations are utilized to group pixels into line-support regions and to determine the location and properties of edges [84] The method is applied for line extraction in [165166] The visualization in Figure 9 is based on source code provided in [166]

Line Extraction Method Resolution lt 5 m Resolution gt 5 m Unknown ResolutionCanny edge detection [75121151167] [129168] [138144169170]

Hough transform [73120126171] [172] [140169173] Line segment detector [128165171] [144145174ndash177]

(a) (b) (c)

Figure 9 Edge detection applied on the greyscale UAV orthoimage based on (a) Canny edge detection and (b) the Laplacian of Gaussian The output is a binary image in which one value represents edges (green) and the other value represents the background (black) (c) Shows the line segment detector applied and imposed on the original UAV orthoimage

224 Contour Generation

This section describes methods that are used to generate a vectorized and topologically connected network through connection of line segments Table 3 shows an exemplary selection of case studies that apply the methods described in the following which can be categorized in two groups

(i) A human operator outlines a small segment of the feature to be extracted Then a line tracking algorithm recursively predicts feature characteristics measures these with profile matching and updates the feature outline respectively The process continues until the profile matching fails

Figure 9 Edge detection applied on the greyscale UAV orthoimage based on (a) Canny edge detectionand (b) the Laplacian of Gaussian The output is a binary image in which one value represents edges(green) and the other value represents the background (black) (c) Shows the line segment detectorapplied and imposed on the original UAV orthoimage

Remote Sens 2016 8 689 12 of 28

This section describes methods that are used to generate a vectorized and topologically connectednetwork through connection of line segments Table 3 shows an exemplary selection of case studiesthat apply the methods described in the following which can be categorized in two groups

(i) A human operator outlines a small segment of the feature to be extracted Then a line trackingalgorithm recursively predicts feature characteristics measures these with profile matching andupdates the feature outline respectively The process continues until the profile matching failsPerceptual grouping explained in the following can be used to group feature characteristicsCase studies that apply such line tracking algorithms can be found in [174178179]

(ii) Instead of outlining a small segment of the feature to be extracted the human operator can alsoprovide a rough outline of the entire feature Then an algorithm applies a deformable templateand refines this initial template to fit the contour of the feature to be extracted Snakes which areexplained in the following are an example for this procedure

Perceptual grouping is the ability to impose structural organization on spatial data based ona set of principles namely proximity similarity closure continuation symmetry commonregions and connectedness If elements are close together similar to one another form a closedcontour or move in the same direction then they tend to be grouped perceptually This allowsto group fragmented line segments to generate an optimized continuous contour [180]Perceptual grouping is applied under various names such as line grouping linking mergingor connection in the case studies listed in Table 3

Snakes also referred to as active contours are defined as elastic curves that dynamically adapta vector contour to a region of interest by applying energy minimization techniques thatexpress geometric and photometric constraints The active contour is a set of points that aimsto continuously enclose the feature to be extracted [181] They are listed here even thoughthey could also be applied in previous steps such as image segmentation [112117] In thisstep they are applied to refine the geometrical outline of extracted features [80131135]

Table 3 Case study examples for contour generation methods

Contour Generation Method Resolution lt 5 m Resolution gt 5 m Unknown Resolution

Perceptual grouping [113115128148182] [168] [141142144145157160177183ndash189]Snakes [80112117190ndash193] [131135178]

225 Postprocessing

Postprocessing aims to improve the output of the delineated feature by optimizing its shapeThree prominent approaches are explained in the following Table 4 shows an exemplary selection ofcase studies that apply the described postprocessing methods which are visualized in Figure 10

Douglas-Peucker algorithm is used to simplify a line by reducing the number of points in a curvethat is approximated by a series of points [194]

Morphological operators are employed as a postprocessing step to smooth the contour of detectedline features [195]

Table 4 Case study examples for postprocessing methods

Postprocessing Method Resolution lt 5 m Resolution gt 5 m Unknown Resolution

Douglas-Peucker algorithm [7279171182196] [133168] [197]

Morphological operators [737579115116120121126128146148196198199] [132] [124125142144200]

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Perceptual grouping explained in the following can be used to group feature characteristics Case studies that apply such line tracking algorithms can be found in [174178179]

(ii) Instead of outlining a small segment of the feature to be extracted the human operator can also provide a rough outline of the entire feature Then an algorithm applies a deformable template and refines this initial template to fit the contour of the feature to be extracted Snakes which are explained in the following are an example for this procedure

Perceptual grouping is the ability to impose structural organization on spatial data based on a set of principles namely proximity similarity closure continuation symmetry common regions and connectedness If elements are close together similar to one another form a closed contour or move in the same direction then they tend to be grouped perceptually This allows to group fragmented line segments to generate an optimized continuous contour [180] Perceptual grouping is applied under various names such as line grouping linking merging or connection in the case studies listed in Table 3

Snakes also referred to as active contours are defined as elastic curves that dynamically adapt a vector contour to a region of interest by applying energy minimization techniques that express geometric and photometric constraints The active contour is a set of points that aims to continuously enclose the feature to be extracted [181] They are listed here even though they could also be applied in previous steps such as image segmentation [112117] In this step they are applied to refine the geometrical outline of extracted features [80131135]

Contour Generation Method Resolution lt 5 m Resolution gt 5 m Unknown Resolution Perceptual grouping [113115128148182] [168] [141142144145157160177183ndash189]

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

Postprocessing aims to improve the output of the delineated feature by optimizing its shape Three prominent approaches are explained in the following Table 4 shows an exemplary selection of case studies that apply the described postprocessing methods which are visualized in Figure 10

Douglas-Peucker algorithm is used to simplify a line by reducing the number of points in a curve that is approximated by a series of points [194]

Morphological operators are employed as a postprocessing step to smooth the contour of detected line features [195]

Postprocessing Method Resolution lt 5 m Resolution gt 5 m Unknown ResolutionDouglas-Peucker algorithm [7279171182196] [133168] [197]

(a) (b) (c)

Figure 10 (a) Douglas-Peucker simplification (red) of the contour generated with snakes (green) The simplified contour approximates the fence that marks the cadastral boundary better than the snake contour does (b) Binary image derived from Canny edge detection as shown in Figure 9a The image

Figure 10 (a) Douglas-Peucker simplification (red) of the contour generated with snakes (green)The simplified contour approximates the fence that marks the cadastral boundary better than the snakecontour does (b) Binary image derived from Canny edge detection as shown in Figure 9a The imageserves as a basis for morphological closing shown in (c) Through dilation followed by erosion edgepixels (green) belonging to one class in (b) are connected to larger regions in (c)

23 Accuracy Assessment Methods

In the following approaches that assess the accuracy of extracted linear features are describedIn order to quantify the accuracy reference data are required to then calculate a metric which measuresthe similarity between the result and the reference data These methods are known as superviseddiscrepancy methods [201] The reference data can be acquired through manual digitation of visuallyextractable linear features [64202203] or through their extraction from existing maps [146199204]Some authors extend the assessment to aspects such as time cost and energy savings and includefurther accuracy measures [64] For methods intending to classify linear features the accuracyassessment is extended to thematic aspects [205206] In such cases the confusion matrix is calculatedas well as statistics derived from it such as the userrsquos producerrsquos and overall accuracy as well asthe kappa coefficient [113207ndash209] The accuracy might also be evaluated based on thematic andgeometric aspects [210] The geometric accuracy incorporates positional aspects indicating errors interms of the objectrsquos location and errors in terms of the spatial extent of an object These componentscan be assessed with pixel-based and object-based measures Pixel-based accuracy assessment hasa rather quantitative character is often used to assess geometric accuracy and is more standardizedthan object-based accuracy assessment The latter has a rather qualitative character and is often usedto assess classification quality [143] The trend towards standardized pixel-based accuracy measuresis manifested in efforts from the International Society for Photogrammetry and Remote Sensing(ISPRS) which publishes benchmark data to assess different methods in a uniform approach [211]A comparison of both approaches shows that object-based approaches provide additional accuracyinformation compared to pixel-based approaches [207] One example for this additional informationare topological aspects that can be assessed with an object-based approach as shown in [212]Such approaches can be based on a fuzzy representation of the objectrsquos boundary [213214] Ultimatelydifferent aspects in terms of feature extraction performance can be highlighted with a combination ofpixel-based and object-based metrics [215]

The following approaches that can be applied both pixel-based and object-based calculateplanimetric accuracy They are simple to implement and are often applied when assessing featureextraction methods [203207215216]

The completeness measures the percentage of the reference data which is explained by theextracted data ie the percentage of the reference data which could be extracted The valueranges from 0 to 1 with 1 being the optimum value

The correctness represents the percentage of correctly extracted data ie the percentage of theextraction which is in accordance with the reference data The value ranges from 0 to 1 with 1being the optimum value

Remote Sens 2016 8 689 14 of 28

The redundancy represents the percentage to which the correct extraction is redundant ieextracted features overlap themselves The value ranges from 0 to 1 with 0 being theoptimum value

The Root-Mean Square (RMS) difference expresses the average distance between the matchedextracted and the matched reference data ie the geometrical accuracy potential of the extracteddata The optimum value for RMS is 0

These planimetric measures calculate per pixel or per object the amount of (i) True Positives (TP)where the extracted features match the reference data (ii) False Positives (FP) where the extractedfeature does not exist in the reference data and (iii) False Negatives (FN) where the features existentin the reference data are not extracted The measures described can be used to derive further qualitymeasures These include the quality the rank distance the branching factor the mean detour factor andconnectivity measures when assessing linear networks [171203217218] Further the precision-recallcurve the F-measure the intersection over union metric (IoU) and the average precision are derivedto assess object detection methods [90] Since they contain the information inherent in the initialmeasures they are used if a single quality measure is desired

In the most common and simple approach a buffer area of a specific width is calculated aroundlinear features in the extracted data and the reference data Comparing these areas then leads tothe described accuracy measures A buffer analysis can be performed either on a pixel-based orobject-based representation of linear features For a pixel-based representation the buffer consists ofa set of pixels within a specific distance from a set of pixels that represents the line For an object-basedrepresentation ie a vector representation the buffer consists of a corridor of a specific width aroundthe line [219] The results of this approach strongly depend on the buffer width

3 Discussion

In the following sections the previously described feature extraction and evaluation methods arediscussed and interpreted in perspective of previous studies and in the context of UAV-based cadastralmapping The former is be based on advantages and drawbacks faced in case studies while the latteris based on the experiences made during the implementation of representative methods

31 UAV-based Cadastral Mapping

As the investigated case studies on feature extraction are rarely UAV-based their potential isdiscussed based on the studies described in the introduction In cadastral mapping UAVs bearadvantages compared to conventional approaches as they provide a tool for fast and cheap captureand visualization of spatial data UAVs can improve the pace of a mapping procedure as data canbe captured and analyzed automatically However the full production chain and quality control ofUAV data may involve substantial time investment Compared to conventional image data used forcadastral mapping UAV-derived orthoimages are more flexible in capture time space and groundresolution Furthermore the orthoimages can be partially updated or reused in further applicationsDue to the research gap in the field of UAV-based automatic feature extraction within the context ofcadastral mapping their feasibility cannot be assessed thoroughly This review serves as a startingpoint to fill that gap and the missing link between UAV-based cadastral mapping and automaticfeature extraction

For all feature extraction approaches it is necessary to define characteristics of the feature to beextracted as stressed in [64220] Defining feature characteristics has been done in this review forboundary features regarding their geometry spectral property topology and context This is oftennot exclusively done in the reviewed case studies which extract linear features such as coastlinesagricultural field boundaries road networks and buildings One example of a feature description

Remote Sens 2016 8 689 15 of 28

approach similar to this review is employed in a study on the extraction of road networks [179]in which the feature description is extended to functional characteristics Even with a thoroughdescription of linear boundary features methods that extract visible features only are not capable ofextracting the entirety of boundary features This might include socially constructed boundaries thatare not visible to an optical sensor [5157] The detection of such boundaries can be supported throughmodel-driven approaches that generate a contour around land plots based on a priori knowledge

The methods reviewed in this study are grouped according to common workflow steps appliedfor linear feature extraction from high-resolution optical sensor data They consist of (i) preprocessing(ii) image segmentation (iii) line extraction (iv) contour generation and (v) postprocessing (Figure 4)An entirety of methods has been reviewed which does not result in a recommendation of one specificmethod per workflow step since further experimental work would be required to do this reliablyThe most common methods listed in this review are applied to data of different resolutions mostlybelow 5 m Furthermore the workflow steps are not necessarily independent and sometimes the samemethod can be applied within different steps Finding a non-redundant classification structure for allmethods does not seem feasible as stated by authors of similar review papers on road extraction [221]object detection [90] and linear feature extraction [64] Many case studies combine strategies from avariety of approaches of which many example combinations are listed in [221] The greatest variety canbe found in case studies on road detection [222] This application field appears to be most prominentfor linear feature extraction demonstrated in the large amount of case studies their comprehensivereview in [221] their extensive comparison on benchmark data [223] and commonly applied accuracyassessment methods originating from this field [216]

In the following sections advantages and drawbacks named in case studies that apply previouslydescribed methods are outlined Furthermore recommendations on their applicability for UAV-basedboundary delineation are drawn when possible

(i) Preprocessing steps that include image enhancement and filtering are often applied in case studiesthat use high-resolution data below 1 m [118121126] This might be due to the large amount ofdetail in such images which can be reduced with filtering techniques Without such preprocessingoversegmentation might resultmdashas well as too many non-relevant edges obtained through edgedetection One drawback of applying such preprocessing steps is the need to set thresholds forimage enhancement and filtering Standard parameters might lead to valuable results but mightalso erase crucial image details Selecting parameters hinders the automation of the entire workflow

(ii) Image segmentation is listed as a crucial first step for linear feature extraction in correspondingreview papers [6477109] Overall image segments distinct in color texture spectral homogeneitysize shape compactness and scale are generally better distinguishable than images that areinhomogeneous in terms of these aspects The methods reviewed in this paper are classifiedinto supervised and unsupervised approaches More studies apply an unsupervised approachwhich might be due to their higher degree of automation The supervised approaches taken frommachine learning suffer from their extensive input requirements such as the definition of featureswith corresponding object descriptors labeling of objects training a classifier and applying thetrained classifier on test data [86147] Furthermore the ability of machine learning approaches toclassify an image into categories of different labels is not necessarily required in the scope of thisworkflow step since the image only needs to be segmented Machine learning approaches such asCNNs can also be employed in further workflow steps ie for edge detection as shown in [224]A combination of edge detection and image segmentation based on machine learning is proposedin [225] A large number of case studies are based on SVM [111] SVMs are appealing due to theirability to generalize well from a limited amount and quality of training data which appears to be acommon limitation in remote sensing Mountrakis et al found that SVMs can be based on fewertraining data compared to other approaches However they state that selecting parameters such

Remote Sens 2016 8 689 16 of 28

as kernel size strongly affects the results and is frequently solved in a trial-and-error approachwhich again limits the automation [111] Furthermore SVMs are not optimized for noise removalwhich makes image preprocessing indispensable for high-resolution data Approaches such asthe Bag-of-Words framework as applied in [226] have the advantage of automating the featureselection and labeling before applying a supervised learning algorithm Further state-of-the-artapproaches including AdaBoost and random forest are discussed in [90]

(iii) Line extraction makes up the majority of case studies on linear feature extraction with Canny edgedetection being the most prominent approach The Canny edge detector is capable of reducingnoise while a second order derivative such as the Laplacian of Gaussian that responds to transitionsin intensity is sensitive to noise When comparing different edge detection approaches it has beenshown that the Canny edge detector performs better than the Laplacian of Gaussian and first orderderivatives as the Robertrsquos cross Sobel and Prewitt operator [162163] In terms of line extractionthe Hough transform is the most commonly used method The LSD appears as an alternative thatrequires no parameter tuning while giving accurate results

(iv) Contour Generation is not represented in all case studies since it is not as essential as thetwo previous workflow steps for linear feature extraction The exceptions are case studies onroad network extraction which name contour generation especially based on snakes as a crucialworkflow step [77221] Snakes deliver most accurate results when initialized with seed pointsclose to features to be extracted [193] Furthermore these methods require parameter tuning interms of the energy field which limits their automation [80] Perceptual grouping is rarely appliedin the case studies investigated especially not in those based on high-resolution data

(v) Postprocessing is utilized more often than preprocessing Especially morphological filtering isapplied in the majority of case studies Admittedly it is not always employed as a form ofpostprocessing to improve the final output but equally during the workflow to smooth the resultof a workflow step before further processing [73120121126] When applied at the end of theworkflow in case studies on road extraction morphological filtering is often combined withskeletons to extract the vectorized centerline of the road [79125142196198]

Considering the accuracy there is not one optimal method for its assessment or reportingMany studies especially those that aim at road extraction use the described approach based onbuffer analysis Quackenbush states that most case studies focus on the extraction of a single featuresuch as roads which reduces the informative value of the resulting confusion matrix [64] Furthermoremany studies report qualitative accuracy measures based on visual assessment Those that providemore quantitative measures are often vague in describing their procedure [64] Moreover Foody statesthat quantitative measures are often misinterpreted and should therefore be interpreted with careHe argues that standardized measures and reporting schemes could be supportive but are unlikelygiven the range of application fields and disciplines [208] The ISPRS benchmark tests are a recenteffort into this direction [211]

Furthermore mixed pixels and registration problems might lead to a gap between extracted dataand reference data This results into low accuracy measures even for accurately extracted features [208]A prioritization of a comprehensive cover of land plots over spatial accuracy is manifested in thefit-for-purpose land administration strategy proposed in [20] The accuracy measure should thereforenot only be interpreted with care but also initially chosen with care taking into account the contextand aim of the study as concluded in [208]

4 Conclusions

This review aimed to explore options to delineate boundaries for UAV-based cadastral mappingAt first an initial review on cadastral mapping based on high-resolution optical sensor data was doneto document the recent state-of-the-art Then cadastral boundary concepts and characteristics were

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summarized Thereafter an extensive review was completed on methods that extract and assess linearfeatures with boundary characteristics Since cadastral features encompass a variety of objects themethods could also be applied to detect linear features in further application fields The workflow stepsproposed for boundary delineation include preprocessing image segmentation line extraction contourgeneration and postprocessing Per workflow step the most popular methods were described and casestudies that have proven their suitability were listed The applicability of some representative methodson high-resolution UAV data was shown through their implementation on an exemplary UAV-derivedorthoimage In general the workflow steps were supported in the majority of case studies and haveproven to be valid when applied on UAV data Thereafter the most common accuracy assessmentapproaches were described Moreover advantages and drawbacks of each method were outlinedresulting in recommendations on their application for UAV-based cadastral mapping

In conclusion this review serves as a basis for the subsequent implementation of mostsuitable methods in a cadastral mapping workflow Depending on the methods chosen and theirimplementation different degrees of automation can be obtained It would be possible to aim for adata-driven workflow that extracts visible boundaries which then need to be processed by a humanoperator Another option would be a model-driven workflow that delineates boundaries based onknowledge about their characteristics Future work should focus on the automation of a suitableworkflow To increase the level of automation while reducing the amount of required human input isalso a central aim of ongoing research [64] Due to a lack of robustness of automatic feature extractionsome authors favor semi-automatic approaches that combine the interpretation skills of a humanoperator with the measurement speed of a computer [178227] Semi-automatic approaches thatinclude editing capabilities seem indispensable for cadastral mapping approaches that focus on theparticipation of local stakeholders and the integration of local knowledge [20] When evaluating anentire boundary delineation workflow for cadastral mapping the following points proposed in [74]can be suggested as a basis for evaluation The workflow should correctly and completely extract allrelevant boundaries be simple in parameterization with a high degree of automation and a minimalneed of interaction have a low computational effort include self-assessment to increase reliabilityand be robust against varying quality of input data The varying quality of input data might resultfrom the application of different UAV platforms and sensors Their influence on the choice of optimalworkflow steps for cadastral mapping could be investigated in future work Furthermore the potentialof integrating information from UAV-derived 3D point clouds might be investigated to extend theproposed workflow

Overall this review contributes to the applicability of UAVs which according to Watts et alhas the potential to revolutionize remote sensing and its application fields to the same degree as theadvent of Geographical Information Systems (GIS) did two decades ago [30] For cadastral mappingnumerous studies have demonstrated the potential of UAVs especially in terms of fast data capturingand high accuracy [40] UAV-based cadastral mapping could contribute to contemporary initiativessuch as the United Nationsrsquo sustainable development goals as it allows a new economic environmentaland social approach to cadastral mapping [228] With UAVs being able to rapidly map small areasthe cadastral map could be kept up-to-date at low-cost in a sustainable way These aspects togetherwith the possibility of creating a transparent and participatory mapping process could contribute toanother recent initiative namely fit-for-purpose land administration published by the World Bank andthe International Federation of Surveyors (FIG) [20]

Future work might concentrate on the integration of existing maps as a source of geometric andsemantic information that was left undetected by the automatic feature extraction Existing maps areincorporated in the workflow of cadastral mapping to support the manual digitization as basis formap updates or for accuracy assessment [232526] Their potential to support the automatic featureextraction as proposed for road extraction [199] is not yet exploitedmdashand hardly investigated [229]As a further data source smart sketchmaps that transfer hand drawn maps into topologically andspatially corrected maps could be integrated in the feature extraction workflow [230] This would allow

Remote Sens 2016 8 689 18 of 28

to integrate local spatial knowledge and to delineate socially perceived boundaries Those boundariesare not visible to optical sensors and were excluded from this review Furthermore the boundarydelineation methods could be enhanced to support the increasingly prominent area of 3D cadastralmapping of boundaries and buildings [66] This would allow a detailed representation of complexinterrelated titles and land uses [50] Future development on UAV-based cadastral mapping can beexpected since the ISPRS lists UAVs as key topic and stresses their potential for national mappingin their recent paper on trends and topics for future work [87] Moreover the European Union hasacknowledged the use of UAV-derived orthoimages as a valid source for cadastral mapping andfurther applications [41]

Acknowledgments This research was funded within its4land which is part of the Horizon 2020 program of theEuropean Union project number 687828 We are grateful to the authors of [24] for providing the UAV orthoimage

Author Contributions Sophie Crommelinck collected and prepared the data for this review paper Markus GerkeFrancesco Nex Rohan Bennett Michael Ying Yang and George Vosselman contributed to the analysis andinterpretation of the data Additionally they supervised the process of defining the structure of this reviewand the selection of case studies The manuscript was written by Sophie Crommelinck with contributions fromMarkus Gerke Francesco Nex Rohan Bennett Michael Ying Yang and George Vosselman

Conflicts of Interest The authors declare no conflict of interest

Abbreviations

The following abbreviations are used in this manuscript

CNN Convolutional Neural NetworksDSM Digital Surface ModelFIG International Federation of SurveyorsFN False NegativesFP False PositivesGIS Geographical Information SystemGNSS Global Navigation Satellite SystemGSD Ground Sample DistanceHRSI High-Resolution Satellite ImageryIoU Intersection over UnionISPRS International Society for Photogrammetry and Remote SensingLSD Line Segment DetectorMRF Markov Random FieldOBIA Object Based Image InterpretationRMS Root-Mean SquareSLIC Simple Linear Iterative ClusteringSVM Support Vector MachineTP True PositivesUAV Unmanned Aerial Vehicle

References

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158 Mokhtarzade M Ebadi H Valadan Zoej M Optimization of road detection from high-resolution satelliteimages using texture parameters in neural network classifiers Can J Remote Sens 2007 33 481ndash491[CrossRef]

159 Mokhtarzade M Zoej MV Road detection from high-resolution satellite images using artificial neuralnetworks Int J Appl Earth Obs Geoinform 2007 9 32ndash40 [CrossRef]

160 Zheng Y-J Feature extraction and image segmentation using self-organizing networks Mach Vis Appl1995 8 262ndash274 [CrossRef]

161 Canny J A computational approach to edge detection IEEE Trans Pattern Anal Mach Intell 1986 8679ndash698 [CrossRef] [PubMed]

162 Juneja M Sandhu PS Performance evaluation of edge detection techniques for images in spatial domainInt J Comput Theory Eng 2009 1 614 [CrossRef]

163 Shrivakshan GT Chandrasekar C A comparison of various edge detection techniques used in imageprocessing Int J Comput Sci Issues 2012 9 269ndash276

164 Hough PV Method and Means For Recognizing Complex Patterns US Patent 3069654 1962 18December 1962

165 Wu J Jie S Yao W Stilla U Building boundary improvement for true orthophoto generation by fusingairborne LiDAR data In Proceedings of the Joint Urban Remote Sensing Event (JURSE) Munich Germany11ndash13 April 2011

166 Von Gioi RG Jakubowicz J Morel J-M Randall G LSD A line segment detector Image Process Line2012 2 35ndash55 [CrossRef]

167 Babawuro U Beiji Z Satellite imagery quality evaluation using image quality metrics for quantitativecadastral analysis Int J Comput Appl Eng Sci 2011 1 391ndash395

168 Turker M Kok EH Field-based sub-boundary extraction from remote sensing imagery using perceptualgrouping ISPRS J Photogramm Remote Sens 2013 79 106ndash121 [CrossRef]

169 Hu J You S Neumann U Integrating LiDAR aerial image and ground images for complete urban buildingmodeling In Proceedings of the IEEE-International Symposium on 3D Data Processing Visualization andTransmission Chapel Hill NC USA 14ndash16 June 2006

170 Hu J You S Neumann U Park KK Building modeling from LiDAR and aerial imagery In Proceedingsof the ASPRS Denver CO USA 23ndash28 May 2004

171 Liu Z Cui S Yan Q Building extraction from high resolution satellite imagery based on multi-scale imagesegmentation and model matching In Proceedings of the International Workshop on Earth Observation andRemote Sensing Applications Beijing China 30 Junendash2 July 2008

172 Bartl R Petrou M Christmas WJ Palmer P Automatic registration of cadastral maps and Landsat TMimages Proc SPIE 1996 [CrossRef]

173 Wang Z Liu W Building extraction from high resolution imagery based on multi-scale object orientedclassification and probabilistic Hough transform In Proceedings of the International Geoscience and RemoteSensing Symposium (IGARSS) Seoul Korea 25ndash29 July 2005

174 Park S-R Kim T Semi-automatic road extraction algorithm from IKONOS images using template matchingIn Proceedings of the Asian Conference on Remote Sensing Singapore 5ndash9 November 2001

175 Goshtasby A Shyu H-L Edge detection by curve fitting Image Vis Comput 1995 13 169ndash177 [CrossRef]176 Venkateswar V Chellappa R Extraction of straight lines in aerial images IEEE Trans Pattern Anal

Mach Intell 1992 14 1111ndash1114 [CrossRef]177 Lu H Aggarwal JK Applying perceptual organization to the detection of man-made objects in non-urban

scenes Pattern Recognit 1992 25 835ndash853 [CrossRef]178 Torre M Radeva P In Agricultural field extraction from aerial images using a region competition algorithm

Proc ISPRS 2000 33 889ndash896

Remote Sens 2016 8 689 26 of 28

179 Vosselman G de Knecht J Road tracing by profile matching and Kalman filtering In Automatic Extractionof Man-Made Objects from Aerial and Space Images Gruen A Kuebler O Agouris P Eds Springer BostonMA USA 1995 pp 265ndash274

180 Sarkar S Boyer KL Perceptual organization in computer vision A review and a proposal for a classificatorystructure IEEE Trans Syst Man Cybern 1993 23 382ndash399 [CrossRef]

181 Kass M Witkin A Terzopoulos D Snakes Active contour models Int J Comput Vis 1988 1 321ndash331[CrossRef]

182 Hasegawa H A semi-automatic road extraction method for Alos satellite imagery Proc ISPRS 2004 35303ndash306

183 Montesinos P Alquier L Perceptual organization of thin networks with active contour functions applied tomedical and aerial images In Proceedings of the International Conference on Pattern Recognition ViennaAustria 25ndash29 August 1996

184 Yang J Wang R Classified road detection from satellite images based on perceptual organization Int JRemote Sens 2007 28 4653ndash4669 [CrossRef]

185 Mohan R Nevatia R Using perceptual organization to extract 3D structures IEEE Trans Pattern AnalMach Intell 1989 11 1121ndash1139 [CrossRef]

186 Jaynes CO Stolle F Collins RT Task driven perceptual organization for extraction of rooftop polygonsIn Proceedings of the Second IEEE Workshop on Applications of Computer Vision Sarasota FL USA 5ndash7December 1994

187 Lin C Huertas A Nevatia R Detection of buildings using perceptual grouping and shadowsIn Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Seattle WA USA21ndash23 June 1994

188 Noronha S Nevatia R Detection and description of buildings from multiple aerial images In Proceedingsof the IEEE Conference on Computer Vision and Pattern Recognition Fort Collins CO USA 17ndash19 June 1997

189 Yang MY Rosenhahn B Superpixel cut for figure-ground image segmentation In Proceedings of the ISPRSAnnals of the PhotogrammetrymdashRemote Sensing and Spatial Information Sciences Prague Czech Republic12ndash19 July 2016

190 Mayer H Laptev I Baumgartner A Multi-scale and snakes for automatic road extraction In ComputerVision-ECCV1998 Springer BerlinHeidelberg Germany 1998

191 Gruen A Li H Semi-automatic linear feature extraction by dynamic programming and LSB-snakesPhotogramm Eng Remote Sens 1997 63 985ndash994

192 Laptev I Mayer H Lindeberg T Eckstein W Steger C Baumgartner A Automatic extraction of roadsfrom aerial images based on scale space and snakes Mach Vis Appl 2000 12 23ndash31 [CrossRef]

193 Agouris P Gyftakis S Stefanidis A Dynamic node distribution in adaptive snakes for road extractionIn Proceedings of the Vision Interface Ottawa ON Canada 7ndash9 June 2001

194 Saalfeld A Topologically consistent line simplification with the Douglas-Peucker algorithm Cartogr GeogrInf Sci 1999 26 7ndash18 [CrossRef]

195 Dong P Implementation of mathematical morphological operations for spatial data processingComput Geosci 1997 23 103ndash107 [CrossRef]

196 Guo X Dean D Denman S Fookes C Sridharan S Evaluating automatic road detection across a largeaerial imagery collection In Proceedings of the International Conference on Digital Image ComputingTechniques and Applications Noosa Australia 6ndash8 December 2011

197 Heipke C Englisch A Speer T Stier S Kutka R Semiautomatic extraction of roads from aerial imagesProc SPIE 1994 [CrossRef]

198 Amini J Lucas C Saradjian M Azizi A Sadeghian S Fuzzy logic system for road identification usingIKONOS images Photogramm Rec 2002 17 493ndash503 [CrossRef]

199 Ziems M Gerke M Heipke C Automatic road extraction from remote sensing imagery incorporatingprior information and colour segmentation Proc ISPRS 2007 36 141ndash147

200 Mohammadzadeh A Tavakoli A Zoej MV Automatic linear feature extraction of Iranian roads fromhigh resolution multi-spectral satellite imagery Proc ISPRS 2004 20 764ndash768

201 Corcoran P Winstanley A Mooney P Segmentation performance evaluation for object-based remotelysensed image analysis Int J Remote Sens 2010 31 617ndash645 [CrossRef]

Remote Sens 2016 8 689 27 of 28

202 Baumgartner A Steger C Mayer H Eckstein W Ebner H Automatic road extraction based on multi-scalegrouping and context Photogramm Eng Remote Sens 1999 65 777ndash786

203 Wiedemann C External evaluation of road networks Proc ISPRS 2003 34 93ndash98204 Overby J Bodum L Kjems E Iisoe P Automatic 3D building reconstruction from airborne laser scanning

and cadastral data using Hough transform ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci 2004 35296ndash301

205 Radoux J Bogaert P Fasbender D Defourny P Thematic accuracy assessment of geographic object-basedimage classification Int J Geogr Inf Sci 2011 25 895ndash911 [CrossRef]

206 Stehman SV Selecting and interpreting measures of thematic classification accuracy Remote Sens Environ1997 62 77ndash89 [CrossRef]

207 Zhan Q Molenaar M Tempfli K Shi W Quality assessment for geo-spatial objects derived from remotelysensed data Int J Remote Sens 2005 26 2953ndash2974 [CrossRef]

208 Foody GM Status of land cover classification accuracy assessment Remote Sens Environ 2002 80 185ndash201[CrossRef]

209 Congalton RG Green K Assessing the Accuracy of Remotely Sensed Data Principles and Practices CRC PressBoca Raton FL USA 2008

210 Lizarazo I Accuracy assessment of object-based image classification Another STEP Int J Remote Sens 201435 6135ndash6156 [CrossRef]

211 Rottensteiner F Sohn G Gerke M Wegner JD Breitkopf U Jung J Results of the ISPRS benchmark onurban object detection and 3D building reconstruction ISPRS J Photogramm Remote Sens 2014 93 256ndash271[CrossRef]

212 Winter S Uncertain topological relations between imprecise regions Int J Geogr Inf Sci 2000 14 411ndash430[CrossRef]

213 Clementini E Di Felice P An algebraic model for spatial objects with indeterminate boundariesIn Geographic Objects with Indeterminate Boundaries Burrough PA Frank A Eds Taylor amp Francis LondonUK 1996 pp 155ndash169

214 Worboys M Imprecision in finite resolution spatial data GeoInformatica 1998 2 257ndash279 [CrossRef]215 Rutzinger M Rottensteiner F Pfeifer N A comparison of evaluation techniques for building extraction

from airborne laser scanning IEEE J Sel Top Appl Earth Obs Remote Sens 2009 2 11ndash20 [CrossRef]216 Heipke C Mayer H Wiedemann C Jamet O Evaluation of automatic road extraction Proc ISPRS 1997

32 151ndash160217 Wiedemann C Ebner H Automatic completion and evaluation of road networks Proc ISPRS 2000 33

979ndash986218 Harvey WA Performance evaluation for road extraction Bull Soc Fr Photogramm Teacuteleacutedeacutetec 1999 153

79ndash87219 Shi W Cheung CK Zhu C Modelling error propagation in vector-based buffer analysis Int J Geogr Inf

Sci 2003 17 251ndash271 [CrossRef]220 Suetens P Fua P Hanson AJ Computational strategies for object recognition ACM Comput Surv (CSUR)

1992 24 5ndash62 [CrossRef]221 Mena JB State of the art on automatic road extraction for GIS update A novel classification

Pattern Recognit Lett 2003 24 3037ndash3058 [CrossRef]222 Tien D Jia W Automatic road extraction from aerial images A contemporary survey In Proceedings of

the International Conference in IT and Applications (ICITA) Harbin China 15ndash18 January 2007223 Mayer H Hinz S Bacher U Baltsavias E A test of automatic road extraction approaches Proc ISPRS

2006 36 209ndash214224 Xie S Tu Z In Holistically-nested edge detection In Proceedings of the IEEE International Conference on

Computer Vision Santiago Chile 7ndash13 December 2015225 Arbelaez P Maire M Fowlkes C Malik J Contour detection and hierarchical image segmentation Pattern

Analysis and Machine Intelligence IEEE Trans Patten Anal Mach Intell 2011 33 898ndash916 [CrossRef][PubMed]

226 Vetrivel A Gerke M Kerle N Vosselman G Identification of structurally damaged areas in airborneoblique images using a visual-Bag-of-Words approach Remote Sens 2016 [CrossRef]

Remote Sens 2016 8 689 28 of 28

227 Guumllch E Muumlller H Hahn M Semi-automatic object extractionmdashLessons learned Proc ISPRS 2004 34488ndash493

228 Hecht AD Fiksel J Fulton SC Yosie TF Hawkins NC Leuenberger H Golden JS Lovejoy TECreating the future we want Sustain Sci Pract Policy 2012 8 62ndash75

229 Baltsavias EP Object extraction and revision by image analysis using existing geodata and knowledgeCurrent status and steps towards operational systems ISPRS J Photogramm Remote Sens 2004 58 129ndash151[CrossRef]

230 Schwering A Wang J Chipofya M Jan S Li R Broelemann K SketchMapia Qualitativerepresentations for the alignment of sketch and metric maps Spat Cognit Comput 2014 14 220ndash254[CrossRef]

copy 2016 by the authors licensee MDPI Basel Switzerland This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC-BY) license (httpcreativecommonsorglicensesby40)

Introduction

Application of UAV-Based Cadastral Mapping

Boundary Delineation for UAV-Based Cadastral Mapping

Objective and Organization of This Paper

Review of Feature Extraction and Evaluation Methods

Cadastral Boundary Characteristics

Feature Extraction Methods

Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing

Accuracy Assessment Methods

Discussion

UAV-based Cadastral Mapping




Conclusions

of archaeological sites and cultural heritage [78] vegetation monitoring in favor of precisionagriculture [910] traffic monitoring [11] disaster management [1213] and 3D reconstruction [14]

Another emerging application field is UAV-based cadastral mapping Cadastral maps arespatial representations of cadastre records showing the extent value and ownership of land [15]Cadastral maps are intended to provide a precise description and identification of land parcels whichare crucial for a continuous and sustainable recording of land rights [16] Furthermore cadastralmaps support land and property taxation allow the development and monitoring of land marketssupport urban planning infrastructure development and allow for producing statistical data Extensivereviews on concepts and purposes of cadastres in relation to land administration are given in [1718]UAVs are proposed as a new tool for fast and cheap spatial data production that enable the productionof cadastral maps Within this field UAVs facilitate land administration processes and contribute tosecuring land tenure [19] UAVs enable a new approach to the establishment and updating of cadastralmaps that contribute to new concepts in land administrations such as fit-for-purpose [20] pro-poorland administration [21] and responsible land administration [22]

11 Application of UAV-Based Cadastral Mapping

In the context of contemporary cadastral mapping UAVs are increasingly argued anddemonstrated as tools able to generate accurate and georeferenced high-resolution imagerymdashfromwhich cadastral boundaries can be visually detected and manually digitized [23ndash25] In order tosupport this manual digitization existing parcel boundary lines might be automatically superimposedwhich could facilitate and accelerate cadastral mapping [26] With the exception of [127] cadastralmapping is not mentioned in review papers on application fields of UAVs [28ndash30] This might be dueto the small amount of case studies within this field the often highly prescribed legal regulationsrelating to cadastral surveys and the novelty of UAV in mapping generally Nevertheless all existingcase studies underline the high potential of UAVs for cadastral mappingmdashin both urban and ruralcontexts for developing and developed countries

In developing countries cadastral mapping contributes to the creation of formal systems forregistering and safeguarding land rights According to the World Bank and the International Federationof Surveyors (FIG) 75 of the worldrsquos population do not have access to such systems Further theystate that 90 countries lack land registration systems while 50 countries are in the process of establishingsuch systems [20] In these countries cadastral mapping is often based on partly outdated maps orsatellite images of low-resolution which might include areas covered by clouds Numerous studieshave investigated cadastral mapping based on orthoimages derived from satellite imagery [2231ndash37] oraerial photography [38] The definition of boundary lines is often conducted in a collaborative processamong members of the communities governments and aid organizations which is referred to asldquoCommunity Mappingrdquo [39] ldquoParticipatory Mappingrdquo [22] or ldquoParticipatory GISrdquo [31] Such outdatedsatellite images are substitutable through up-to-date high-resolution orthoimages derived from UAVsas shown in case studies in Namibia [24] and Rwanda [23] The latter case shows the utility of UAVs topartially update existing cadastral maps

In developed countries the case studies focus on the conformity of the UAV datarsquosaccuracy with local accuracy standards and requirements [4041] Furthermore case studies tendto investigate possibilities of applying UAVs to reshape the cadastral production line efficiency andeffectiveness [42ndash44] In the latter manual boundary detection with all stakeholders is conducted inan office eliminating the need for convening all stakeholders on the parcel In developed countriesUAV data are frequently used to update small portions of existing cadastral maps rather than creatingnew ones Airspace regulations are the most limiting factor that hinder the thorough use of UAVsCurrently regulatory bodies face the alignment of economic information and safety needs or demandsconnected to UAVs [3045] Once these limitations are better aligned with societal needs UAVsmight be employed for further fields of land administration including the monitoring of publicinfrastructure like oil and gas pipelines power lines dikes highways and railways [46] Nowadays

221 Preprocessing

(a) (b)

Remote Sens 2016 8 689 10 of 28

Remote Sens 2016 8 689 10 of 27

(a) (b) (c)

223 Line Extraction

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

221 Preprocessing

(a) (b)

Remote Sens 2016 8 689 10 of 28

Remote Sens 2016 8 689 10 of 27

(a) (b) (c)

223 Line Extraction

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

221 Preprocessing

(a) (b)

Remote Sens 2016 8 689 10 of 28

Remote Sens 2016 8 689 10 of 27

(a) (b) (c)

223 Line Extraction

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

221 Preprocessing

(a) (b)

Remote Sens 2016 8 689 10 of 28

Remote Sens 2016 8 689 10 of 27

(a) (b) (c)

223 Line Extraction

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

221 Preprocessing

(a) (b)

Remote Sens 2016 8 689 10 of 28

Remote Sens 2016 8 689 10 of 27

(a) (b) (c)

223 Line Extraction

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

221 Preprocessing

(a) (b)

Remote Sens 2016 8 689 10 of 28

Remote Sens 2016 8 689 10 of 27

(a) (b) (c)

223 Line Extraction

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

221 Preprocessing

(a) (b)

Remote Sens 2016 8 689 10 of 28

Remote Sens 2016 8 689 10 of 27

(a) (b) (c)

223 Line Extraction

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

(a) (b)

Remote Sens 2016 8 689 10 of 28

Remote Sens 2016 8 689 10 of 27

(a) (b) (c)

223 Line Extraction

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 10 of 28

Remote Sens 2016 8 689 10 of 27

(a) (b) (c)

223 Line Extraction

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 11 of 28

Remote Sens 2016 8 689 11 of 27

(a) (b) (c)

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 12 of 28

225 Postprocessing

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 13 of 28

Remote Sens 2016 8 689 12 of 27

Snakes [80112117190ndash193] [131135178]

225 Postprocessing

(a) (b) (c)

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 14 of 28

3 Discussion

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 15 of 28

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 16 of 28

4 Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 17 of 28

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 18 of 28

Abbreviations

References

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 19 of 28

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 20 of 28

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 21 of 28

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 22 of 28

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 23 of 28

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 24 of 28

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 25 of 28

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 26 of 28

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 27 of 28

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Remote Sens 2016 8 689 28 of 28

Introduction







Preprocessing

Image Segmentation

Line Extraction

Contour Generation

Postprocessing


Discussion





Conclusions

Documents

Review of Automatic Feature Extraction from High-Resolution … · 2018-12-08 · trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy