15arspc Submission 154

8/8/2019 15arspc Submission 154

1/13


2/13


3/13


4/13


5/13


6/13


7/13

7

The method can be applied to burn scars using the Normalised Burn Ratio(NBR) (Lopez-Garcia and Caselles, 1991). When used in a 2-scene changedetection between the burnt scene and a pre-burn scene the NBR provides avery useful index. The NBR is defined as:

74

74

TM TM

TM TM NBR

where TM4 is Landsat band 4 (near infra-red), and TM7 is Landsat band 7(short-wave infra-red).

Areas where the NBR values have increased dramatically in contrast to the pre-fire image are used to provide the seed values, while observations of the NBRvalues where the burn transitions to unburnt provide the edge values.

Segmentation and Definition of the Area of Interest

Image segmentation using a Definiens multiresolution approach provides asimple starting point to break an image into useful, analysable objects (Benz etal, 2004). Once an image is segmented each object is tested against thefeature edge value to find which objects may contain the feature. By identifyingpossible feature objects the processing time is reduced as large areas of ascene tha t dont contain the feature of interest are removed from the analysis.

Building the Gaussian Model for the Feature Each candidate object containing the feature is broken into single-pixel objectsto allow pixel-based processing in an object-oriented framework. The pixelobjects are tested against the input seed value to find the feature seeds. Eachpixel object adjacent to a seed or another feature object is then tested againstthe edge value to find more of the feature. This is an iterative process, loopinguntil either no more pixel objects are available or no pixel objects meeting thevalues can be found.

Analysis of a standard normal distribution shows that the mean 1.97 standarddeviations represents approximately 98% of the distribution. Hence applying the1.97 multiplier results in conservatively capturing the entire set. To develop themodel the pixel objects classified as the feature are statistically analysed to findtheir mean and standard deviation. The eventual target is for the seed value tobe modelled as the feature mean and the edge value as the mean 1.97

standard deviations.This model continues to be built as the process loops through each object.Each subsequent object to be analysed is tested against the original seed valueand the changing edge value until no possible objects remain. Following this asecond pass is executed where new possible candidates are tested against thetarget model having the mean as the seed and mean 1.97 standarddeviations as the edge, until the possible feature objects are once againexhausted. In this way the analysis starts from a conservative guess andproceeds to be increasingly well defined by the discovered feature objects.


8/13

8

Refining the Feature Classes According to Fuzzy Functions Classifying t he feature into various degrees of purity allows modelling of themixtures between the feature and its surroundings. In the case of water thisprovides classes defining mixtures with vegetation or bare areas. The outputclasses then provide an understanding of the flooded features. In the case ofburn scar mapping this provides classes defining various degrees of burnseverity inferred from the magnitude of the remaining vegetation signalobserved in the burnt area.

More importantly building a model without making allowances for mixtures isrelatively unconstrained and may become mathematically divergent and resultin the model developing to unacceptable values. To constrain the model, themost common confounding features are modelled as influencing factors in a setof fuzzy functions. In the case of water the simplest two undesirable featuresare vegetation and soil, which may be modelled using NDVI for vegetation and

longer SWIR wavelengths (such as Landsat band 7) for soils. A series ofclasses is created with functions in the key spectral bands defining how eachband or ratio changes with the changing values of the data. As the SWIRreflectance increases the corresponding changes in NDVI and longer SWIRdetermines whether the feature is still pure water, a mixture of features or thebookend class of soil or vegetation. The fuzzy classification system withinDefiniens compares the fuzzy functions of the classes according to the valuesof the object and assigns the object the class having the highest classificationvalue. The bookend classes are then excluded from the statistical analysis andthe model is constrained.

Reclassification of all Previously Classified Objects Since the feature evolves throughout the modelling process the model changesaccording to how many feature objects are discovered in the image. To ensurethe model acts consistently across the entire image, the process is repeated byreclassifying all objects according to the final values derived by the model. Theseed is modelled as the mean of all features except the bookend classes andthe edge is modelled as the mean 1.97 standard deviations.

More Generic Implementation for Multiple Features Instead of modelling for a single feature mixing with confounding features themethodology extends into multiple target features by finding basic seeds foreach feature and then computing a model for each simultaneously. The featureedges computed using the mean 1.97 standard deviations approach thenform the limits of multiple Gaussian distributions to be compared in the fuzzyclassification process.

The looped approach was applied to several satellite images during the 2009and 2010 floods in Queensland and New South Wales, and the 2009 bushfiresin Victoria. The analysed images included Landsat 5 imagery of the Paroo Riverin northern New South Wales, acquired on 16 th January 2010, and the Victorianbushfires, acquired on 17 th February 2009. The model was implemented in aDefiniens Enterprise Server environment using 5 processing engines in parallel.


9/13

9

ResultsThe requirement for the Paroo River scene was the extraction of the extent offlood water. A subset of the image and associated classification result from themodel are shown in Figure 4.

The model detected water bodies or continuous streams repeatably andreliably. Further analysis of neighbouring scenes showed that the results aredirectly comparable between images, with flood events across multiple scenesproducing consistent extents. The fuzzy classes provide information on floodedfeatures which we interpreted as indicating the presence of mud or waterhidden by vegetation.

However the model generally underestimates the water where channelsbecome very thin compared to the resolution of the sensor, or the reflectancevalues for a feature in the imagery exceed the modelled seed thresholds. Inthese areas feature seeds are not formed so the model cannot grow. It ispossible that the water depth in these features is so shallow that the band 5reflectance is dominated by non-water features. Processing time from the startof the model to the creation of the output as an ArcGIS shapefile wasapproximately 20 minutes.

The requirement for the Victorian bushfire scene was the extraction of the totalburnt area. Processing time was similar to the Paroo flood imagery atapproximately 20 minutes. A subset of the image and associated classificationresult from the model are shown in Figure 5. The result shows that the modelgenerally underestimates the burnt area where the burn was the least intense.However it does provide a class distinction between where the fire burned the

canopy completely and where the canopy remained after the fire.


10/13

10

Figure 4: Subset of the Landsat 5 imagery acquired over the Paroo River on 16 th January 2010. The top panel shows the original image displayed as bands 5, 4 and 2as Red, Green and Blue. The bottom panel shows the results of the water detection

model with dark blue areas as open water, green areas as a water-vegetation mix andyellow areas as a water-soil mix. The fuzzy classification of the water has highlighted

the flooded vegetation in the main water body, and shown the soil-water mixture inseveral of the shallow areas in the lower part of the image.


11/13

11

Figure 5: Subset of the Landsat 5 imagery acquired over the burn area of the VictorianFires on 17 th February 2009. The top panel shows the original image displayed as

bands 5, 4 and 2 as Red, Green and Blue. The bottom panel shows the results of theburn scar detection model with red areas representing totally burnt and yellow areas

representing less burnt areas that feature a vegetation signal.


12/13

12

Discussion and ConclusionIn our experience natural features rarely have a normal distribution. TheGaussian approximation is computationally fast, simple to implement, andworks well on features that are continuous across the landscape. This methodis unlikely to be helpful where features are discrete and spatially separated.None the less the flood mapping results do identify small farm dams providedthey are large enough with respect to the spatial resolution of the sensor andthe contained water is not completely confounded by other features.

The use of fuzzy classification to un-mix the contributions of various features tothe reflectance of an object allows for a more informative output. Flood mapscan include classes describing submerged vegetation or mud while burn scarmaps can include the distinction between understory and canopy burns. Thetransitional classes created in this flood mapping process have been used byNSW SES to help plan the location of medical and food drops for impacted

communities during the recent 2010 floods in northern NSW. They found thatthe mixed classes indicated areas that were liable to be difficult to traverse.

The fuzzy-Gaussian model implemented in object-oriented analysis provides afast method of extracting features from imagery with little user intervention.Previous examination of flood imagery using a more traditional density slice ofband 5 (Frazier and Page, 2000), took hours to complete and was very userintensive, producing many errors of commission. Further the results were nottransferrable to other scenes. The modelled method repeatably analyses fullscenes in under 30 minutes and produces consistent results between scenes.

The method has proved to be applicable to different image types including

Landsat, MODIS and SAR, providing a standard method for feature extractionwith minimal user input. This method is useful where results need to begenerated quickly and accuracy is not critical, such as in emergency responsesituations and where large volumes of data need to be analysed quickly inbatch processes with repeatable results.


13/13

13

ReferencesBenz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004,Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. Journal of Photogrammetry & Remote Sensing, 58:239-258.Frazier, P.S., and Page, K.J., 2000, Water body detection and delineation withLandsat TM data, Photogrammetric Engineering & Remote Sensing, 66:1461-1467.

Lopez-Garcia, M.J., and Caselles, V., 1991, Mapping burns and naturalreforestation using Thematic Mapper data, Geocarto Inernational, 6:31-37.

Xiong, B., Zhang, X., and Jiang, W., 2009, Semi-supervised classificationbased on gauss mixture model for remote imagery. Proceedings of the ISPRS Wuhan 2009 Workshop: Virtual Changing Globe for Visualisation and Analysis.

Documents

15arspc Submission 154