Volume 3, Issue 11, November 2013 ISSN: 2277 128X ...ijarcsse.com/Before_August_2017/docs/papers/Volume_3/11...Volume 3, Issue 11, November 2013 ISSN: 2277 128X International Journal

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Volume 3, Issue 11, November 2013 ISSN: 2277 128X

International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com

An Effective Multi-feature Fusion Object-based Classification Method on

ArcGIS Platform Using Very High-resolution Remote Sensing Image Ling Zhang

* Mariofanna Milanova

Department of IT Service Department of Computer Science

City of Houston, USA University of Arkansas at Little Rock, USA

Abstract— We propose a novel, multi-feature fusion, object-based classification approach on the ESRI ArcGIS

platform using very high resolution remote sensing imagery for the study area of Lake Maumelle watershed,

Arkansas, USA. The Lake Maumelle watershed contains a reservoir that provides drinking water for more than

400,000 people in the Little Rock metropolitan area. The reservoir has a long history of excellent water quality.

However, suburban and urban development is increasing within the watershed, prompting concerns that land use

changes will impact water quality. The goal of this research is to build an easy to use tool to help water resource

managers detect land use changes within the watershed. We suggest that the integration of GIS and Remote Sensing

technologies on a single GIS platform could be a cost - effective approach in terms of time and money. The image

classification method includes four steps. First, texture features of the image are extracted, analyzed, and composited

into the original image. The original image is a color-infrared aerial imagery with one foot ground resolution

acquired in 2009. Second, we use an ESRI ArcObjects Model based on a multiresolution statistical segmentation

algorithm to generate regions. Third, training samples and test samples are generated using ESRI ArcMap and visual

inspection of the image. Next, a supervised support vector machine (SVM) classifier is applied to the segmented

multi-feature fusion and mulit–channels image. Fourth, classification accuracy is assessed with the test samples using

a tool developed in ArcGIS platform. The experiment achieved a high overall accuracy of 94.44 with 0.91 Kappa

value, which indicated an excellent probability that image pixels were correctly classified.

Keywords— GIS, remote sensing, segmentation, classification, texture

I. INTRODUCTION

There is a need for user-friendly processes to detect land use changes for water resources managers. Land use in the

Lake Maumelle watershed, Arkansas, USA, is expected to undergo significant changes in the next several decades, with

residential developments replacing forest in many areas. These land use changes have the potential to increase pollutant

loads and degrade water quality in the lake. Detecting the land use and land cover (LULC) changes is therefore a critical

requirement for the monitoring of the hydrology and water quality of the watershed and developing effective land

management and planning strategies. Current methods for detecting LULC changes require technical expertise, the use of

multiple software platforms and can be quite expensive in terms of both time and money. The traditional approach uses

visual interpretations of remote sensing data to search and identify changes, followed by manual editing of the LULC

feature class using geographic information systems (GIS) software. For Lake Maumelle, the existing LULC GIS database

was obtained with the help of 2009 color infrared one foot resolution orthophotos using unsupervised classification in

ERDAS imagine image processing software and digitizing the classified features using ArcGIS software. Image

classification refers to the task of extracting information classes from a multiband raster image [1]. It is a process used to

convert remotely sensed data into meaningful information. Post-classification comparison is a popular change detection

technique [2]. A highly accurate image classification of LULC is the key to the success of change detection in the study

area.

We are going to use the same image dataset that were used to develop existing LULC GIS database for our project.

Very high resolution imagery (VHRI) can provide much more detailed ground truth and much better visualization.

However, due to the fact that complexity and redundancy of the very high resolution imagery (VHRI) is greatly increased

and detailed information such as spectral, shape, context, and texture are provided by VHRI, traditional pixel-based

image classification approaches may not be feasible for VHRI and cannot provide satisfying results. Object – based

approaches can overcome the limitations of pixel-based methods applied to VHRI. Therefore, in this paper, we propose a

multi-feature fusing, object-based classification method, which uses texture feature fusing, image segmentation based on

multispectral statistics and image object shape measurement, and supervised support vector machine classification.

These components are implemented and deployed on the ESRI ArcGIS 10 platform. The integration of image processing

and GIS on a single software platform can save both time and money. We will use a 2009 color infrared orthophoto with

1 foot ground resolution as sample image data and a LULC feature class from the existing LULC GIS database that

covers the sample area for our proposed classification method experiment.

http://www.ijarcsse.com/

Zhang et al., International Journal of Advanced Research in Computer Science and Software Engineering 3(11),

November - 2013, pp. 10-23


II. IMAGERY DATA AND ITS NDVI AND TEXTURE FEATURES

The original image is a color-infrared aerial image with one foot ground resolution acquired in 2009. It contains three

individual bands being sensitive to red, green, and near infrared wavelengths. Near infrared (NIR) wavelengths are

slightly longer than red, and they are outside of the range visible to the human eye. Blue wavelengths are filtered out of

CIR. Conventionally, a CIR image is set up to display the infrared band data with a red tone. Red wavelengths will

appear green, and green wavelengths will appear blue. Blue wavelengths are not displayed. Because the healthy green

vegetation will appear to be bright red, a CIR image is also known as a “false colour” image. CIR tends to penetrate

atmospheric haze better than natural colour, and it provides sharper imagery [3].

The Normalized Difference Vegetation Index (NDVI) is an important feature of digital CIR data, and is one of the

major attributes for representing vegetation conditions, and it is widely utilized to distinguish the vegetation covered

surface and non-vegetation covered surface [3].

The formula is:

NDVI = (NIR –Red)/(NIR + Red),

where NIR is the Near Infrared channel, and Red is the Red channel. The NDVI image is generated using ArcGIS NDVI

function. Fig. 2.1 shows the original CIR aerial photo and its NDVI image.

Fig. 2.1 Original CIR image (left) and NDVI image (right)

The textural features of image contain information about the spatial distribution of spectral variability within a

band. Texture is one of the important characteristics used in identifying objects or regions of interest in an image. Grey

Level Co-occurrence Matrices (GLCM) is one of the earliest methods for texture feature extraction proposed by Haralick

et al., in 1973 [4] and is one of the most popular methods used for describing texture. This method looks at computing a

spatial dependence probability distribution matrix [4]. This method assumes that information about image texture is

adequately specified by the matrix of relative frequencies Pij with the two gray cells separated by a distance d and an

angle alpha occur on the image, one with the gray level i and the other with the gray level j [4]. Using the gray level

spatial dependence matrices various texture features like energy, entropy, contrast, homogeneity and correlation are

calculated. Such textural descriptors are still developed today to give more and more powerful tools for classification

tasks or segmentation problems [5].

In order to describe how gray level co-occurrence matrices work, we let I(x, y: 0 ≤ x ≤N-1, 0 ≤ y ≤N-1) be used to

represent an image of size N * N with G gray levels. The image I with G gray levels is quantized to Ng levels, and let Lx

= {1, 2, 3…, Nx} be used to represent the horizontal spatial domain (range of pixel values) and Ly = {1, 2, 3…, Ny} be

used to represent the vertical spatial domain [4].

The image block used to derive gray level co-occurrence matrices is based on the nearest neighbourhood resolution

cells [4]. The neighbourhood resolution cells for a pixel * at I(x, y) are shown in Fig. 2.2.

Fig. 2.2 Resolution cells 1 and 5 are horizontal nearest neighbours to resolution cell *; resolution cells 2 and 6 are 135°

nearest neighbours; resolution cells 3 and 7 are 90° nearest neighbours; and resolution cells 4 and 8 are 45° nearest

neighbours to * [4].


November - 2013, pp. 10-23


In this method it is assumed that texture information is adequately specified by a matrix of relative frequencies Pij

where two neighbouring resolution cells that are separated by a distance d and an angle α occur in the image block, one

with a gray level i and the other with a gray level j [4]. These matrices are therefore a function of the angular relationship

between neighbouring cells as well as the distance between the cells [4].

Mathematically this relationship can be represented as:

P (i, j, d, 0°) = # {((k, l), (m, n) Є N, where k – m = 0, |l – n| = d

P (i, j, d, 45°) = # {((k, l), (m, n) Є N, where k – m = d, l – n = -d

P (i, j, d, 90°) = # {((k, l), (m, n) Є N, where |k – m| = d, l – n = 0

P (i, j, d, 135°) = # {((k, l), (m, n) Є N, where k – m = -d, l – n = d

where # represents the number of elements in the set, and k, l, m, n Є G and I (k, l) = i, I (m, n) = j.

To illustrate the above with an example, let us consider a simple 4 * 4 image chip with 3 gray levels:

The gray level co-occurrence matrices for the above image chip where d = 1 and α = 0°, 90°, 45° and 135°

respectively are shown below:

P(0°, H) P(90°,V) P(45°,RD) P(135°,LD)

The gray tone spatial-dependence matrix (GLCM) can be computed after the neighbourhood resolution cells for a

pixel have been obtained. The gray level co-occurrence matrix will be normalized by the sum of all the elements in the

co-occurrence matrix [4].

Haralick defined a total of 14 parameters from the co-occurrence matrix. Only five of them - homogeneity and angular

second moment, contrast, correlation, Entropy, local homogeneity (dissimility) are commonly used because it was shown

that the other out of the 14 are highly correlated with each other, and that the 5 indicated sufficed to give good results in a

classification task [6]. The homogeneity and angular second moment is a measure of similarity in pixel values of the

neighbourhood resolution cells in an image block [4]. The contrast feature is a measure of the amount of local variations

present in an image [4]. The correlation measure is a measure or predictability of pixel values in the horizontal and

vertical domains. It could also be described as a measure of linear dependencies in an image [4]. Entropy can be

described as a measure of the complexity or the measure of information in an image. The greater the variations in the

neighbourhood resolution cells, the greater the entropy values [4].

The following table (Table 1) lists 5 Haralick’s texture feature equations.

TABLE I: HARALICK’S TEXTURE FEATURE EQUATIONS

Name Equation Equation #

Angular Second Moment

(1)

Contrast

(2)

Correlation

(3)

Inverse Difference Moment (Dissimility)

(4)

Entropy

(5)

1 1 0 0

1 1 0 0

0 2 2 2

3 3 2 2


November - 2013, pp. 10-23


P(i,j) is the (ith

, jth

) entry in the normalized co-occurrence matrix; Ng is the dimension of co-occurrence matrix; Pᵪ(i)

and Pᵧ(j) are the marginal probabilities.

𝜇𝑥 , 𝜇𝑦 are the means of pₓ and pᵧ:

σᵪ and σᵧ are the standard deviations of Pᵪ and Pᵧ:

In our method, we propose to use standard deviation of pixel values in the neighbourhood to extract texture feature

from the very high resolution remotely sensed imagery using Block Statistics tools in ArcGIS.

The equations

(6)

σ(і,ј) is the standard deviations of P(i,j) of the N x N neighbourhood. The normalization function can be a mean filter:

𝑦 𝑖, 𝑗 = 𝑋 𝑖 − 𝑚, 𝑗 − 𝑛 ∗ 𝑅(𝑚, 𝑛)1𝑛=−1

1𝑚=−1 (7)

The Block Statistics tool in ArcGIS 10 performs a neighborhood operation that calculates a statistic for input cells

within a fixed set of non-overlapping windows or neighborhoods. The standard deviation is calculated for all input cells

contained within each neighborhood. The resulting value for an individual neighborhood or block is assigned to all cell

locations contained in the minimum bounding rectangle of the specified neighborhood. For a 5 * 5 image block, the

standard deviation image block is illustrated as Fig. 2.3:

III. SEGMENTATION

Segmentation refers to the process of partitioning a digital image into non-intersectable regions, in the way that each

region is homogeneous and that union of. It is an essential step toward higher level image processing [7]. Because very

high resolution remotely sensed imagery contains much more complexity and redundancy of spectrum, shape, texture,

the traditional pixel based classification methods tend to produce classification errors such as multiple spectral signatures

within a semantic object. Those multiple signatures, cannot be effectively dealt with by standard methods and tend to

produce “salt and pepper” classification results, when one semantic object is composed of multiple spectral signatures [8].

Baatz et al., proposed the multiresolution segmentation algorithm, in which the concept of the “degree of fitting” was

defined [9].

𝑕 = (𝑓1𝑑 − 𝑓2𝑑 )2𝑑 (8)

The distance can be furthermore standardized by the standard deviation over all segments of the feature in each

dimension.

𝑕 = 𝑓1𝑑−𝑓2𝑑

𝜎𝑓𝑑

2

𝑑 (9)

Appropriate object features can be mean spectral values or texture features such as the variance of spectral values

[9].

The degree of fitting of two adjacent image objects can be defined by describing the change of heterogeneity in a

virtual merge and generalized to an arbitrary number of channels c, each having a weight Wϲ:

(10)

Appropriate definitions for spectral heterogeneity of image objects can be the variance of spectral mean values or the

standard deviation spectral mean values [9].

Changhui et al., defined the distance of two regions in their research:

2 5 7 8 8

6 6 7 9 9

5 5 4 9 9

4 6 3 2 2

6 6 9 9 9

2 2 2 2 2

2 2 2 2 2

2 2 2 2 2

2 2 2 2 2

2 2 2 2 2

Fig. 2.3 original 5x5 image block Standard deviation image block

mk:@MSITStore:C:\PROGRA~2\ArcGIS\DESKTO~1.1\Help\SPABF7~1.CHM::/009z000000qp000000.htm


November - 2013, pp. 10-23


(11)

Ii and Ij are the average spectral value of two regions. If the spectral differences between neighbour regions are smaller

than the threshold, the regions would merged be together [10].

The eCognition commercial software adopts the Fractal Net Evolution Approach for segmentation, which is a

region merging technique based on a pairwise region merging. The merging criterion is that the average heterogeneity of

image objects weighted by their size in pixel should be minimized [9].

Due to the facts that much more efforts may be required to determine the mulitspectral difference threshold or

heterogeneity minimum threshold, we need to look at a decision to merge two regions based on spectral statistics:

(12)

is the set of regions with Ɩ pixels, , Ɩ is the total number of pixels in the image. is the total

number of pixels in this region R, , g = 256, is a set of independent random variables with

values in [0, g/ ]. The merging predicate is:

(13)

denotes the observed average for channel a in region R [11].

This algorithm is able to capture the main structural components of imagery using a simple but effective statistical

analysis, and it has the ability to cope with significant noise corruption, handle occlusions with the sort function, and

perform multi-scale segmentation. But it has the shortage of under-segmentation [12].

In very high resolution remote sensing images, the image objects that represent ground geographic features or

objects are clear. Therefore, we must handle the image objects homogeneity or heterogeneity during the process of region

merging. Shape is an important property of an object in an image. It can be used to measure the homogeneity or

heterogeneity of image objects that represent the geographic features.

Shape heterogeneity can be described by the difference of degree of compactness and/0r degree of smoothness

before and after two adjacent regions are merged [9]. Compactness represents the cluster degree of the pixels in the

region. Smooth degree indicates the smoothness of the region boundary. Shape criteria can reduce the disturbance from

noise and fragments and result in more regular objects [13].

(14)

where c is the compactness, is the perimeter, and A the object area.

(15)

where s is the smoothness, is the perimeter, and b is the perimeter of the region bounding box [13].

(16)

are weight values about compact heterogeneity and smooth heterogeneity respectively [13].

For two adjacent regions intending to merge, the merge criteria based on shape heterogeneity can be:

(17)

For each band of a multispectral image data, the general heterogeneity is:

(18)

In the study area, less than 10% land use are urban areas, where most of shapes are more regular such as linear

(roads), square or rectangles (residential or commercial areas) with smoother edges than other geographic features.

Around these paved or unpaved areas are bare-ground features with lighter colours. Forest, water bodies, and rooftops

have darker colours. Therefore, the shape criteria for two regions to be merging are set to be dynamic. For developed

areas and their surrounding areas, the shape threshold for merging two regions will be high. A non-linear function will

give the shape threshold based on the average spectral values of two regions to be determined to merge. The formula and

Fig. 3.1 graph for the function are:

(19)

where d is initial value of parameter, when R =255 , W < d , to avoid possible ln(0), If ,(RMax R ) < 10 then


November - 2013, pp. 10-23


Fig. 3.1 Graph for formula (19)

Finally, for an arbitrary number of channels c and at n scale, we have a formula from formula (17) and (12):

(20)

Fig. 3.2 shows the flowchart of multiresolution statistical segmentation.

Scale < n ?

Merge regions

yes

High resolution

remote sensing

data

Segmented image

Merge predicate: spectral statistics and

shape heterogeneity

yes

no

Fig. 3.2 Mulitresolution statistical segmentation flowchart

IV. CLASSIFICATION

Support vector machine (SVM) technique has advantages in remote sensing: (a). It has the ability to generalize well

from a limited amount and / or quality of training data. This property is particularly appealing in the remote sensing field

in that training samples and ground truthing are limited and expensive [14]. (b) it is a non-parametric learning technique,

therefore there is no assumption made on the underlying data distribution. This is particularly appealing in remote

sensing applications since data acquired from remotely sensed imagery usually have unknown distributions [15]. (c). It

can produce higher classification accuracy than the traditional methods such as maximum likelihood estimation [16],

decision trees, neural networks k-nearest neighbour (k-NN), training data-driven fuzzy classifiers [17]-[19]. (d). It can

incorporate texture properties well and achieve higher classification performance with image texture [26] and in the

object – based classification approaches [25][27][28].


November - 2013, pp. 10-23


In addition, the SVM algorithm for data classification is based on structural risk minimization (SRM) principle.

SVM can handle large input spaces, which means it is good for processing remote sensing data. It can effectively avoid

overfitting by controlling the margin, and automatically identify a small subset made up of informative points – support

vectors. The Support vector machine classification technique has been widely used in land cover land use tasks

[29][30][31], forest species classification [32][33][34], and urban areas[35][36][37].

Here is the basic idea behind SVM for pattern recognition. For the two-class classification problem, support vector

machine is a linear two-class classifier. Given a set of samples:

𝑀 = 𝑥1 , 𝑦1 , 𝑥2 , 𝑦2 , … , 𝑥𝑖 , 𝑦𝑖 , i is the number of the samples i = 1, 2, 3, ….l,

𝑥𝑖𝜖 𝑅𝑛

and

𝑦 𝜖 {+1, −1}𝑙 Here, +1 and -1 indicate the two classes. The processing of classification is to produce a solution function:

𝑓 𝑥 : 𝑥𝑖 → 𝑦

For any other model of x, f(x) can produce the corresponding values of y better. In the condition that a linear separation is

possible, we have the solution as shown in Fig. 4.1

Fig. 4.1 The separating hyperplanes.

For a dataset such as shown in Figure 4.1 is linearly separable, there exist many such hyperplanes that can separate

the two classes correctly. So we have the question that which hyperplane to choose to ensure not only the training data,

but the future examples as well, are classified correctly. Support vector machine (SVM) can achieve the hyperplane that

is known as maximal margin hyperplane which is maximally distant from the two class data. Fig. 4.2 shows maximal

margin hyperplane. In Figure 4.2, we let the L- to be ⍵·x + b = -1, and L+ to be ⍵•x + b = +1, then the maximal margin

hyperplane can be expressed as to L0 = ⍵·x + b = 0, where is the weight vector of classification’s surface, and the scalar

b is called the bias. The interval between the two classes is 2

||⍵||, which is the distance between L- and L+. The calculation

of the maximal margin hyperplane can be formulated to be the following optimization problem:

minω,b 𝜔 2

Subject to 𝑦𝑖 ⍵ · 𝑥𝑖 + 𝑏 ≥ 1, 𝑖 = 1,2,··· 𝑛 (21)

Let to be the solution of the above optimization problem. The maximal margin hyperplane can be expressed

by , and the classification decision function can be formulated by [32].

Fig. 4.2 The maximal margin hyperplane

The training samples which satisfy the equation are named support vectors. To optimize, the

Lagrange function can be converted to Quadratic programming [32].

(22)


November - 2013, pp. 10-23


Where αi ≥ 0 is named Lagrange multiplier. The objective function and constraints can be expressed by the equation (23).

max 𝐿 𝜶 = 𝜶𝑖 −1

2

𝒏

𝒋=𝟏

𝒏

𝒊=𝟏

𝑛

𝑖=1

𝒚𝒊𝒚𝒋𝜶𝒊𝜶𝒋𝒙𝒊 ∙ 𝒙𝒋 subject to 𝜶𝑖𝑦𝑖

𝑛

𝑖=1

= 0, 0 ≤ 𝜶𝑖 ≤ 𝐶 (23)

If we let 𝛼𝑖∗ to be the optimal solution, then ⍵∗ = 𝛼𝑖

∗𝑛𝑖=1 𝑦𝑖𝑥𝑖 and the optimal classification decision can be

calculated by the equation (24).

𝐿 = 𝛼𝑖𝑦𝑖𝑥𝑖𝑇𝑥𝑗 + 𝑏 𝑏 = 𝑦𝑖 − 𝛼𝑖𝑦𝑖𝑥𝑖

𝑇𝑥𝑗

(24)

In many applications, if a linear separation is not possible, a non-linear function can provide better accuracy by

mapping the train samples into high-dimensional feature space in which the linear separation of the train samples is

possible. To do so, a function: 𝜙 𝑥 is introduced. The mapping is performed by a kernel function

which defines an inner product in the higher dimension space. Therefore, the optimal classification decision

function implemented by SVM can be written as equation (25) [32].

𝑓 𝑥 = 𝑠𝑔𝑛( 𝛼𝑖∗𝑦𝑖𝑘 𝑥𝑖 , 𝑥𝑗 + 𝑏∗

𝑘

𝑖=0

)

(25)

The can be unknown exactly to use the equation (25). The kernel function 𝑘 𝑥𝑖 , 𝑥𝑗 can do it. There are two

typical kernel functions: polynomial kernel (equation 26) and Radial Basic function (RBF) kernel (equation 27)

𝑘 𝑥𝑖 , 𝑥𝑗 = ( 𝑥𝑖 , 𝑥𝑗 + 𝑟)𝑑 (26)

𝑘 𝑥𝑖 , 𝑥𝑗 = exp −ϓ 𝑥𝑖 − 𝑥𝑗 2

, ϓ > 0 (27)

ϓ, d, r are kernel parameters.

V. EXPERIMENT

We extracted the standard deviation texture feature image. After inspecting the histogram, statistics and images of

the five types of texture images - angular second moment, contrast, dissimility, entropy and correlation, we make a

comparison with angular second moment, contrast, dissimility (Table II, Fig. 5.1 – Fig. 5.4)

Fig. 5.1 standard deviation texture feature (left) and Contrast texture feature image (right)

Fig. 5.2 dissimility (inverse difference moment) texture feature (left) and angular second moment texture feature (right)


November - 2013, pp. 10-23


Fig. 5.3 Histograms of contrast texture feature (left) and standard deviation texture feature (right)

Fig. 5.4 Histograms of angular second moment texture feature (left) and dissimility texture feature (right)

TABLE II

TEXTURE FEATURES IMAGES STATISTICS

Texture feature Maximum Minimum Mean Std dev.

Standard Deviation 255 0 38.8 24.2

Contrast 255 0 12 22.75

Angular second

moment

1 0 0.19 0.83

Dissimility 32 0 1.99 1.97

We found the standard deviation texture feature is similar to contrast texture features.

Histogram of g25demoenvctrstgray.tif: Field = VALUE

Distribution based on display resolution

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Histogram of g25demosecmgray.tif: Field = VALUE


14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Histogram of g25demoenvdisgry.tif: Field = VALUE


50,000

45,000

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0


November - 2013, pp. 10-23


We implemented multiresolution statistical segmentation algorithm using ArcObjects Model on ArcGIS version 10.

Fig. 5.6 shows an example of a segmented image at resolution 2400 pixels (top) and 800 pixels (bottom).

Fig. 5.6 segmented imagery at 2400 pixels resolution (upper) and 800 pixels resolution (lower).

We also used the algorithm to segment texture feature and NDVI images. Fig. 5.7 shows a segmented NDVI image

at 1600 pixels resolution and 400 pixels resolution. Fig. 5.8 shows a segmented standard deviation texture feature image

at 1600 pixels resolution and 400 pixels resolution.

Fig. 5.7 Segmented NDVI image at 1600 pixels resolution (bottom) and 400 pixels resolution (top).


November - 2013, pp. 10-23


Fig. 5.8 segmented standard deviation texture feature at 1600 pixels resolution (top) and 400 pixels resolution (bottom).

There are six categories of land use land cover in the area of the experiment aerial photo according to the existing

LULC GIS database (Table 3).

TABLE III: LULC TYPES IN THE EXPERIMENT AERIAL PHOTO AREA

LULC types Coniferous

forest

Deciduous

forest

Pasture/bare

ground

Lake/pond Paved

surface

Rooftop

class C4 C2 C1 C3 C5 C7

acres 402 0.51 74.7 5.8 313.9 0.46

percentage 50.4% 0.06% 9.4% 0.73% 39.3% 0.06%

We created 30 training sample points for each type using the Create Random Points tool in ArcMap, which

generated about 0.032% sampling pixels relative to the total number of pixels in the test image. An ArcPy tool using

LIBSVM [33][34] with RBF was used to process the SVM classification with the segmented image that was fusioned

with the standard deviation texture feature and NDVI images. Fig. 5.10 shows the classification result image.

Fig. 5.10 classification result image


November - 2013, pp. 10-23


VI. ACCURACY ASSESSMENT

We generated about 30 test points for each classification types using Create Random Points tool in ArcMap and

visual inspection on the image. The accuracy of classifications was measured using the overall accuracy.

Given the error matrix N = (nij), the overall accuracy is defined as

(28)

Where |T| is the number of pixels we are testing.

Given the error matrix with r rows, the Kappa statistic is defined as:

(29)

where i = 1, 2,3…., r , and r is the number of rows in the matrix, X ii s the number of observations in row i and column i,

Xi+ = ∑Xj+ , and X+i = ∑Xik are the marginal totals for row i and column i respectively, and N is the total number of the

observations.

Using an accuracy assessment tool developed with ArcPy in ArcGIS, we obtained an error matrix (Table 4),

including overall, user's, and producer's accuracies, as well as Kappa statistic for a given classification based on test data.

TABLE IV: ERROR MATRIX

C4 C1 C3 C2 C5 C7 UT UP

C4 366 14 0 0 1 0 381 96.06

C1 4 301 0 0 6 0 311 96.78

C3 0 0 27 0 0 0 311 100

C2 12 7 0 55 0 0 74 74.32

C5 0 1 0 0 14 0 15 93.33

C7 0 1 0 0 0 18 19 94.74

PT 382 324 27 55 21 18 827 0.00

PP 95.81 92.90 100 100.00 66.67 100.00 0.00 94.44

UT is user's total, PT is producer's total, and UP and PP are corresponding percentages. The results were a 94.44

overall accuracy and 0.91 Kappa value. The assessment results indicate we achieved an excellent probability that the

image pixels were correctly classified with very strong agreement.

VII. CONCLUSIONS

In this paper, we propose a novel, multi-feature fusion, object-based classification approach on the ESRI ArcGIS 10

platform using very high resolution remote sensing imagery for the study area of Lake Maumelle watershed, Arkansas,

USA. There is only one supervised, pixel-based classification method – The Maximum Likelihood Classification, and

one unsupervised classification method - ISO Cluster unsupervised Classification in ArcGIS software. We extracted

standard deviation texture feature and NDVI feature images from original image data and fusioned into original image

data. We implemented multiresolution statistical segmentation algorithm on ArcGIS 10 platform. The multiresolution

statistical segmentation algorithm improved the existing statistical region merge model with image objects shape

heterogeneity dynamic thresholds, which overcame the advantage of under-segmentation. The SVM with the RBF kernel

function are used to classify the image objects or regions produced by the multiresolution statistical segmentation process.

Our experiment proved the proposed approach can achieve high classification performance for very high resolution

remotely sensed image. The method greatly improves the processing speed of the SVM training and classifying. This

method is based on the integration of GIS and Remote Sensing technologies on a single GIS platform, which can be a

cost - effective approach in terms of time and money.

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