Classification of Satellite broadcasting Image and Validation Exhausting Geometric Interpretation

Classification of Satellite broadcasting Image and Validation Exhausting

Geometric Interpretation

M. Srinivasa Rao

Kartheek V. L

Dr. T. Madhu

1,2

Assistant Professor

Principal

1

Department of Computer Science Engineering

1,2,3

Swarnandhra College of Engineering and Technology

Abstract— Classification of Land Use/Land Cover (LULC) data from satellite images is extremely remarkable to design the thematic maps for analysis of natural resources like Forest, Agriculture, Water bodies, urban areas etc. The process of Satellite Image Classification involves grouping the pixel values into significant categories and estimating areas by counting each category pixels. Manual classification by visual interpretation technique is accurate but time consuming and requires field experts. To overcome these difficulties, the present research work investigated efficient and effective automation of satellite image classification. Automated classification approaches are broadly classified in to i) Supervised Classification ii) Unsupervised Classification iii) Object Based Classification. This paper presents classification capabilities of K-Means, Parallel Pipe and Maximum Likelihood classifiers to classify multispectral spatial data (LISS-4). Using statistical inference, classified results are validated with reference data collected from field experts. Among three, Maximum Likelihood classifier (MLC) gained a significant credit in terms of getting maximum Overall accuracy and Kappa Factor.

Key words: Land Use Land Cover, Pixel, Classification, LISS-4, Overall accuracy, Kappa Factor

I. INTRODUCTION

Satellite imagery is a basis of large amount of two dimensional information is recorded by satellite sensor. Satellite images are rich and play a crucial role in providing geographical information [1]. Satellite and remote sensing images provides quantitative and qualitative information that reduces sophistication of field work and study time [2]. Satellite remote sensing technologies collect temporal data in the form of images at regular intervals. The volumes of data receive at datacenters is huge and it is growing exponentially as technology is growing rapid speed as timely and data volumes and data volumes have been emergent at an epidemic rate [3]. There is a strong need of well-organized and constructive mechanisms to extract and interpret valuable information from massive satellite images. Satellite image classification is a powerful technique to

extract information from enormous number of satellite images.

Satellite image classification is the process of coalition the pixels in to meaningful subdivision based on its numeric values [4]. Satellite image classification involves interpretation of remote sensing images, Spatial data mining to study about various natural recourses like Forest, Agriculture, Water bodies, Urban areas and determining various land uses in an area[5].

This paper is structured in assorted sections. Section-II describes the Hierarchy of Satellite image classification techniques. Section-III explains the various classification methods. Section-IV describes about the study area and data sources. Section-V presents validation of results using statistical inference. Results and Discussions are provided in Section-VI. The final section endows the conclusion.

II. SATELLITE IMAGE CLASSIFICATION TECHNIQUES

Based on the spatial resolution, satellite images are categorized in to Low (coarser pixel), Medium (medium pixel size) and High (Finer pixels) resolution satellite images (see Figure 1).

Fig. 1: Low, Medium And High Spatial Resolution Satellite Image.

There are several methods and techniques for satellite image taxonomy (see Figure 2). These methods are generally classified in to three categories [6].

Fig. 2: Hierarchy Of Satellite Image Classification Techniques

A. Manual Classification

Manual classification techniques are Robust, efficient and effective technique because the analysts will do the classification by visual interpretation based on ground reality of study area. But this method consumes more time and requires field experts. The accuracy and efficiency of the classification, depends on the analyst knowledge and familiarity towards the field of study.

B. Automatic Classification

Performance of satellite image classification using Visual interpretation depends on the analyst. To avoid this problem, classification is done automatically by grouping the pixels based on its similarity and dissimilarity. Based on the spatial resolution of satellite image, automated satellite image classification methods further classified in to three categories. (a) Pixel Based Classification (b) Sub-Pixel Based Classification (c) Object Based Classification.

1) Pixel Based Classification:

As the typical remote sensing image classification technique, Pixel based classification methods assume each pixel is pure and typically labeled as a single land use and land cover type [7] [8]. Using this method, remote sensing imagery is considered a collection of pixels with spectral information, and there by spectral variables and their

transformations are input to pre-pixel classifier. In general pixel based classification can be classified in to two groups. a) Unsupervised Classification:

With unsupervised classifiers, a remote sensing image is divided into number of classes based on the natural groupings of image pixel values without having the training data or prior knowledge of study area[9][10]. Two most commonly used unsupervised classification algorithms, K-Means[11][12] and its variant, the Iterative Self-Organizing Data analysis (ISODATA) technique. Recently, Support Vector Machine (SVM) and hierarchal clustering methods were also developed for unsupervised classification [13]. The major drawback with this unsupervised classification is computationally intensive and insufficient accuracy in getting meaningful and required classes.

b) Supervised Classification:

Fig. 3: Flow Chart For Supervised Classification Of Satellite Image

2) Sub Pixel Based Classification:

Pixel Based image classification techniques assumes that only one land use land cover type exists in each image pixel. Due to heterogeneity of landscape with respect to spatial resolution, such an assumption is not valid for classification of satellite images having Medium and coarse spatial resolution [14]. As a better alternative Sub-Pixel classification techniques are appropriate as spatial proportion of each land use land cover type can be accurately estimated [15]. Major Sub-Pixel classification techniques are fuzzy classification, neural Networks [16] [17], regression modeling [18], regression tree analysis [19] and spectral mixture analysis [20] have been developed to address the mixed pixel problems.

3) Object Based Classification:

Compared to Pure-Pixel and Sub-Pixel classification methods, Object Based classification provides a new prototype to classify remote sensing imagery [21][22]. Instead of individual pixels, object based classifiers considers Geographical object as basic unit for analysis. Object based methods generate image objects through image segmentation [23], and then conduct image classification on objects rather than pixels. With image segmentation techniques, image objects are formed using spectral, spatial and contextual information. Object based approaches are considered more appropriate for Very High Resolution (VHR) remote sensing images since they assume that geographic objects are formed by multiple image pixels. Many studies are proven significant higher accuracy has been achieved with object based approaches [24][25][26].

Fig. 4: (A) Satellite Image (B) Object Based Classification Of Satellite Image (C) Pixel Based Classification Of

Satellite Image

III. SATELLITE IMAGE CLASSIFICATION METHODS

This section exemplifies few up to date satellite image classification methods.

K-Means: In data mining, K-Means clustering [27] is a process of unsupervised classification (i.e. Cluster) analysis. This method aims to partition n-observations in to k-clusters in which each scrutiny belongs to the cluster with the adjacent mean. It is an iterative course of action. In first step, it assigns an arbitrary preliminary cluster vector. In second step, each pixel classifies to closest cluster. Finally, the novel cluster mean vectors are intended based on each and every pixel in one cluster. Second and final steps are repeated until no change in mean value of each cluster. The objective of K-Means algorithm is to play down the within cluster changeability.

The objective function is Sum of Squares Distances (see eq-1) between each pixel and its assigned cluster center shown as,

SSdist = ∑ (1)

Where , is the mean of the cluster pixel x is assigned to.

S atellite Im age

Geo-S patial DataBase

Raster Data

P r e-P r ocessing

S am ple V ector Data shape File

Classification

Gener ate S ignatur e File

Tr aining Data P ixel Data

Classified Im age

Confusion Matr ix V alidation Classification

S chem a

S top

By Minimizing the Mean Squared Error (MSE) , SSdist can

be minimized (see eq-2). Cluster variability can be measured using MSE as,

MSE = ∑ =

(2)

A. Iterative Self Organizing Data (ISODATA):

ISODATA [28] algorithm allows a set of all clusters to be robotically adjusted during the iteration by assimilation of similar clusters and Splitting clusters (see Figure 5). Assimilation of Clusters is done if whichever the number of pixels in the cluster is less than a confident threshold or else the centers of two clusters are closed than a certain threshold. Splitting of a Cluster is done if the standard deviation of Cluster exceeds its threshold value and number of pixels is twice the threshold of minimum number of pixels.

Fig. 5: ISODATA Classification Pixels Using Cluster Means

ISODATA algorithm is comparable to K-Means algorithm [29] with the dissimilarity that the ISODATA algorithm allows for diverse number of clusters. Whereas the K-Means considers that all the clusters are acknowledged.

B. Support Vector Machine (SVM):

SVM [30] is a classification system resulting from statistical learning theory. It separates the classes with decision surface that maximizes the margin between the classes. The surface is often called the optima hyper plane, the data points neighboring to hyper plane are called support vectors (see Figure 6). By maximizing the margin between data points and decision boundary Misclassification errors can be minimized [33].

A Binary classification of N training samples and each example is consisting of a tuple (xi, yi) (i= 1,2...,N)

where, xi=(xi1,xi2,....xid)T corresponds to the attribute set for

the ith sample and let yi denotes its class label.

Fig. 6: Margin Of Decision Boundary In Binary SVM Classifier

The decision boundary for linear classifier can be written as ⃗⃗⃗ ⃗ (3)

If we can label all the circles as class +1 and all the stars as class -1 then we can predict the class label "y" for any test sample "z"

y= { ⃗⃗ ⃗⃗

The Margin (d) of the decision boundary (see eq-4)is given by the distance between these two hyper planes

d=

(4)

C. Minimum Distance Classification:

Minimum distance to means [31] approach is supervised classification approach in which the decision rule calculates the spectral distance between the measurement vector for the candidate pixel and mean vector for each signature(see Figure 7). this classifier is suitable when each class has one representing vector[34].

Fig. 7: Calculation Of Minimum Distance Between Centers Of Three Classes And Candidate Pixel With Respect To

Bands A&B

The distance (see eq-5) can be calculated and the candidate pixel is assigned to the class with the smallest spectral Euclidian distance (Minimum distance) to the candidate pixel [32].

Dab = ∑ (5) Where, Dab= Distance between class a and pixel b,

ai = Mean spectral value of class a in band i, bi= Spectral

value of pixel b in band i, n= Number of spectral bands.

D. Mahalanobis Distance Classifier:

Mahalanobis Distance classifier [34] [35] is almost same as Minimum distance approach. It uses Covariance matrix for satellite image classification.

Mahalanobis Distance (Dx) =

∑ (6)

Where, ∑= Pixel Covariance matrix for class i (i=1,2,...n), =Average vector of class i.

E. Parallel Piped Classification:

Fig. 8: Classification Of Pixel Data Based On Lowest (µa1-2s), Highest (µa1+2s) And Mean (µa1) Values On Band A And Lowest (µb1-2s), Highest (µb1+2s) And Mean (µb1)

Values On Band B Of Class-1 Using Parallel Piped Approach.

In two-dimensional space this will form many rectangular boxes as number of classes. All the pixels which fall inside the box are labeled to that class. Computationally parallel piped classifier is efficient but overlaps may leads to misclassifications.

F. Maximum Likelihood Classification (MLC):

MLC is also known as Bayesian classifier, is a statistical method for supervised classification [32] in which pixels with maximum likelihood is classified in to corresponding class. The likelihood (Lk) of a pixel (see eq-7) belongs to a

class k is measured in terms of its posterior probability [ 37].

Lk =

( )

( )

Where, =prior probability of class k, ( )= Conditional probability or Probability density function of class K.

( ) are common to all classes. So, Lk

depends on probability density function.

Fig. 9: Concept Of Maximum Likelihood Classification Based On Probability Density With Respect To Each Class

Probability density function (see eq-8) for normal distribution to calculate likelihood can be expressed as follows.[38]

Lk(X) =

√

₍₈₎

Where, Lk(X)=Likelihood of pixel X belongs to

class k,n= Number of satellite image bands and = variance-covariance matrix of class k.

G. K-Nearest Neighbor Classifier:

KNN classification [36][37] is based on majority vote of the K- nearest Neighbors, based on Euclidean distance (see eq-9) in feature space, where K specifies the number of neighbors to be used. It does not require a training step to be performed.

Let (x, y) D --> Set of training examples k --> Number of nearest neighbors z=(x', y') --> Test example

Euclidean Distance

d(x', x)= ∑ (9)

Once the nearest neighbor list is obtained, the test example is classified based on the majority class of its nearest neighbor (see eq-10) .

Majority voting :

y' =argmax ∑ (10) Where, --> class label

--> Class label for one of the nearest neighbors.

Drawback of KNN classifier is that some test records may not be classified because they do not match any training example.

H. Seeded Region Growing (SRG):

In SRG technique [40] the image is segmented in to regions with respect to set of g seeds. Given set of seeds S={s1,s2...,sg}, each step additional pixel is included into

one of the seed sets. Furthermore, these initial seed are replaced by the centroids of these generates homogeneous regions R = {R1, R2, ... Rg} with reference to further pixels

gradually. The pixels in indistinguishable region are labeled as one class and pixels in dissimilar regions labeled by different classes, and others be called unallocated pixels [41].

Set of Unallocated pixels (H) is formulated as (see eq-11): H={ ⋃ | ⋃ } (11) is defined as the difference among the testing pixel (x , y) and its adjacent labeled region .

= ₍₁₂₎ Where, indicates the values of three color components of the testing pixel and represents the average of the three color components of the homogeneous region , with the centroid of .

IV. STUDY AREAS AND DATA SOURCES

A. Study Areas:

surrounding areas in the middle of Eluru city and Study Area-II located between 160 40' 38.10" N 810 08'35.39" E and 160 38' 57.03" N 810 09' 43.74"E (see Figure 11) covers the Aqua and Agriculture fields at outs cuts of Eluru city.

Fig. 10: Study Area-I Shown In Blue Window In Satellite Image.

Fig. 11: Study Area-II Shown In Blue Window On Satellite Image.

B. Reference Data:

In order to estimate the exactness of the classification under taken in this research, reference data was captured by digitizing different areas like Urban, Aqua and Agriculture etc. using visual interpretation by the field expert [47]. To evaluate Study Area-I, Five different classes (see Figure 12) are digitized and for Study Area-II, Six different classes (see Figure 13) are digitized in the form of polygons.

Fig. 12: Reference Data of Study Area-I

Fig. 13: Reference Data of Study Area-II

V. VALIDATION

Accuracy assessment [43] of the Satellite image classification techniques can be undertaken using confusion matrix (see Figure 14) and Kappa statistics. The Kappa index of agreement (KIA) is a statistical measure adopted for accuracy assessment in Land Use & Land Cover analysis of satellite image. It is often used to check for accuracy of classified satellite images verses real ground truth data. All diagonal elements of the confusion matrix (see Figure 14 ) represents classified pixels that are agreed to ground truth and all non-diagonal elements represents error of omission(exclusion) or error of commission(inclusion) [44].

Reference Data

(Ground truth) Row

Total

C1 C2 C3 C4 C5 C6

C

las

sif

ied

d

ata C_C1 N11 N12 N13 N14 N15 N16 N1+

2 N21 N22 N23 N24 N25 N26 N2+

C3 N31 N32 N33 N24 N35 N36 N3+

C4 N41 N42 N43 N44 N45 N46 N4+

C5 N51 N52 N53 N54 N55 N56 N5+

C6 N61 N62 N63 N64 N65 N66 N6+

Colum

Total N+1 N+2 N+3 N+4 N+5 N+6 N

Fig. 14: A Model Of Confusion Matrix For Six Classes Error of Omission is a ratio between number of correctly assigned pixels in each class and the number of training set pixels used for that class. It is also called Producer accuracy ( ).

(13)

Where, = Number of pixels that are correctly classified to class Ci and = Number of pixels in the reference data class Ci.

Error of Commission is a ratio between number of correctly assigned pixels in each class and the total number pixels assigned to the same class. It is also called User accuracy ( ).

(14) Where, = Number of pixels that are correctly classified to class Ci and = Number of pixels in the classified data class Ci.

Op =

∑

(15)

Kappa statistic is a measure of the difference between the actual agreement between Reference data and an automated classifier and the chance agreement between the reference data and random classifier.

K= = ∑ ∑

∑ (16) Kappa factor value ranges from 0 to 1. A Kappa value of zero represent that the classification is poor. If the chance agreement is large, kappa value could be negative, indicates very poor classifier performance. Based on the value of the Pixel each one is labeled with a class name by using a classification technique. K-Means Unsupervised classifier with k=5 is applied on Study Area-I and its classification results (See Figure 15) shows Blue: Water Body, Green: Agriculture, Yellow with Dots: Urban Area, Red: Trees, Light Green: Grass Land.

For both Supervised classification methods (i.e. Parallel Pipe and Maximum Likelihood Classifiers) Signature data is generated from the Reference data with specified number of classes.

Fig. 15: Classified image of Study Area-I using K-Means Classifier

Using pixel value range, Parallel Pipe classifier generated the classification results (see Figure 16). By calculating likelihood of each pixel with respect to each class and pixel with maximum likelihood is classified into one of the five classes (see Figure 17). Maximum likelihood is decided by using conditional probability. In both cases the classification results shows Blue: Water Body, Green:

Agriculture, Yellow with Dots: Urban Area, Red: Trees, Light Green: Grass Land.

Fig. 16: Classified image of Study Area-I using Parallel Pipe Classifier.

Fig. 17: Classified image of Study Area-I using MLC Classifier

Validation of each class is done by comparing classified date with Reference data (see Figure 12) having 50 sample pixels in which 14 pixels represents water body, 11 pixels represents Urban area, 7 pixels for Grass land, 6 for Trees and 12 pixels are for Agriculture. For all three classification methods Confusion Matrix is formulated and producer and User accuracy for each class is computed. To evaluate correctness of classification Overall accuracy and Kappa factor are computed for K-Means ( see Table 1), Parallel Pipe (See Table 2) and Maximum Likelihood Classifier (see Table 3). The diagonal elements of the confusion matrix represent the pixels that are correctly classified and non diagonal elements are miss classified pixels with respect to ground truth (i.e. Reference data).

Reference Data

(Ground truth) Row Total

Water Body Urban Area Grass Land Trees Agriculture

C

las

sif

ied

d

ata Water Body 11 1 0 0 0 12

Urban Area 1 7 1 0 1 10

Grass Land 2 1 4 1 1 9

Trees 0 2 1 4 1 8

Agriculture 0 0 1 1 9 11

Colum Total 14 11 7 6 12 50

Class

Omission Error (Producer

Accuracy )

Commission Error (User Accuracy )

Overall Accuracy

Water

Body 78.57% 91.60%

70% Urban

Grass Land 57.16% 44.44%

Trees 66.66% 50.00%

Agriculture 75% 81.80%

Kappa =0.621

Table 1: Confusion Matrix And Kappa Factor To Validate Classes Of Study Area-I Using K-Means Classifier.

Reference Data

(Ground truth) Row Total

Water Body Urban Area Grass Land Trees Agriculture

C

las

sif

ied

d

ata Water Body 11 1 0 0 0 12

Urban Area 2 8 2 0 0 12

Grass Land 1 1 4 1 1 8

Trees 0 1 1 5 1 8

Agriculture 0 0 0 0 10 10

Colum Total 14 11 7 6 12 50

Class

Omission Error (Producer Accuracy )

Commission Error (User Accuracy )

Overall Accuracy

Water

Body 78.57% 91.66%

76.6% Urban

Area 72.72% 66.66%

Grass Land 57.14% 50.00%

Trees 83.33% 62.50%

Agriculture 83.33% 100.00%

Kappa =0.69

Table 2: Confusion Matrix And Kappa Factor To Validate Classes Of Study Area-I Using Parallel Pipe Classifier.

Reference Data

(Ground truth) Row Total

Water Body Urban Area Grass Land Trees Agriculture

C

las

sif

ied

d

ata Water Body 12 2 0 0 0 14

Urban Area 1 8 1 0 0 10

Grass Land 1 1 6 1 1 10

Trees 0 0 0 5 1 6

Agriculture 0 0 0 0 10 10

Colum Total 14 11 7 6 12 50

Class

Omission Error (Producer Accuracy

)

Commission Error (User Accuracy )

Overall Accuracy

Water

Body 85.71% 85.71%

82% Urban

Area 72.72% 80.00%

Grass Land 85.71% 66.66%

Trees 83.33% 83.33%

Agriculture 83.33% 100.00%

Kappa =0.82

Table 3: Confusion Matrix And Kappa Factor To Validate Classes Of Study Area-I Using MLC Classifier. To check the performance, The same Three

Fig. 18: Classified image of Study Area-II using K-Means Classifier

Secondly, Parallel Pipe classifier is applied on Study Area-II by using Six classes Signature data and the classification results (see Figure 19) shows Yellow: Creek, Blue: Aqua Ponds, Pink with Dots: Sandy area, Light green: Grass Area, Red: Trees, Green: Agriculture.

Fig. 19: Classified Image Of Study Area-II Using Parallel Pipe Classifier.

Finally, Maximum Likelihood classifier is applied on Study Area-II by using Six classes Signature data.

Classification results (see Figure 20) shows Yellow: Creek, Blue: Aqua Ponds, Pink with Dots: Sandy area, Light green: Grass Area, Red: Trees, Green: Agriculture.

Fig. 20: Classified Image Of Study Area-II Using MLC Classifier.

Validation of each class is done by comparing classified date with Reference data (see Figure 13) having 57 sample pixels in which 9 pixels represents Creek, 13 pixels represents Aqua Ponds, 8 pixels for Sandy land, 7 for Grass land, 5 for Trees and 15 pixels are for Agriculture. For all three classification methods Confusion Matrix is formulated and producer and User accuracy for each class is computed. To evaluate correctness of classification Overall accuracy (see eq-15) and Kappa factor (see eq-16) are computed for K-Means ( see Table 1), Parallel Pipe (See Table 2) and Maximum Likelihood Classifier (see Table 3).

Reference Data

(Ground truth) Row Total

C Q S G T A

C

las

sif

ied

d

ata C 6 2 1 0 0 1 10

Q 1 9 2 0 0 1 13

S 1 1 4 1 0 0 7

G 1 1 1 5 1 1 10

T 0 0 0 1 3 1 5

A 0 0 0 0 1 11 15

Colum Total 9 13 8 7 5 15 57

Class

Omission Error (Producer Accuracy )

Commission Error (User Accuracy )

Overall Accuracy

C 66.66% 60%

66.66%

Q 69.23% 69.23%

S 50% 57.14%

G 71.4% 50%

T 60% 60%

A 73.33% 73.33%

Kappa =0.585

C=Creek, Q=aQua Ponds , S=Sandy Land, G= Grass Land, T= Trees, A= Agriculture

Reference Data

(Ground truth) Row Total

C Q S G T A

C

las

sif

ied

d

ata C 6 0 0 0 0 1 7

Q 1 11 1 1 0 0 14

S 1 1 6 0 0 1 9

G 1 1 1 6 0 1 10

T 0 0 0 0 4 0 4

A 0 0 0 0 1 12 13

Colum Total 9 13 8 7 5 15 57

Class

Omission Error (Producer Accuracy )

Commission Error

(User Accuracy )

Overall Accuracy

C 66.66% 85.7%

78.94%

Q 84.61% 78.57%

S 87.5% 66.66%

G 85.7% 60%

T 80% 100%

A 80% 92.3%

Kappa =0.741

C=Creek, Q=aQua Ponds , S=Sandy Land, G= Grass Land, T= Trees, A= Agriculture

Table 5: Confusion Matrix And Kappa Factor To Validate Classes Of Study Area-I Using Parallel Pipe Classifier Reference Data

(Ground truth) Row Total

C Q S G T A

C

las

sif

ied

d

ata

C 7 1 0 0 0 1 9

Q 1 11 1 1 0 1 15

S 1 0 6 0 0 1 8

G 0 1 1 6 0 0 8

T 0 0 0 0 5 0 5

A 0 0 0 0 0 12 12

Colum Total 9 13 8 7 5 15 57

Class

Omission Error

(Producer Accuracy )

Commission Error

(User Accuracy )

Overall Accuracy

C 77.77% 77.77%

82.45%

Q 84.6% 73.33%

S 75% 75%

G 85.7% 75%

T 100% 100%

A 80% 100%

Kappa =0.784

C=Creek, Q=aQua Ponds , S=Sandy Land, G= Grass Land, T= Trees, A= Agriculture

Table 6: Confusion Matrix And Kappa Factor To Validate Classes Of Study Area-II Using MLC Classifier.

VI. RESULTS AND DISCUSSION

LISS-4 satellite images are pre-processed and classified using three methods: K-Means, Parallel Pipe and Maximum Likelihood classifiers. The accuracies of the three methods

Classification Technique

Study Area-I Study Area-II

Overall Accurac

y

Kapp a Facto

r

Overall Accurac

y

Kapp a Facto

r

K-Means 70% 0.62 66.66% 0.585

Parallel piped

classification 76.6% 0.69 78.94% 0.741

Maximum Likelihood classification(ML

C)

82% 0.82 82.45% 0.784

Table 7: Overall Accuracy And Kappa Factor For Classified Study Area-I&II Using Three Classification Methods.

Fig. 21: Graphical Representation Of Overall Accuracy For The Two Study Areas Using Three Classifiers

Judging by the overall accuracy (see Figure 21), it is evident that Maximum Likelihood classifier (MLC) is superior than parallel pipe classifier and also K-Means unsupervised classifier in classification of both study areas (i.e. Overall accuracy 82% vs {76.6%, 70%}for study area-I and 82.45% vs {78.94%, 66.66%}) for study area-II.

Fig. 22: Graphical Representation Of Kappa Factor For The Two Study Areas Using Three Classifiers

In case of Kappa factor (see Figure 22), it is obvious that Maximum Likelihood classifier (MLC) is superior than Parallel Pipe classifier and also K-Means unsupervised classifier in classification of both study areas (i.e. Kappa factor is 0.82 vs {0.69, 0.62} for study area-I and 0.78 vs {0.74, 0.58}for study area-II). If the study area is having heterogeneity in land use and no signature is available then K-Means unsupervised classifier is very much suitable. This research work is also found that K-means classifier is computationally expensive and it needs number of classes (K) as input. If the value of K is fail to spot, leads

to misclassification. When compare to supervised classifiers, K-means classifier showing minimum overall accuracy of 70% & 66.66% for both study area-I and study area-II respectively. By referring kappa factor also K-Means classifier is showing 0.62 & 0.58 for both study area-I and study area-II respectively and it is comparatively less. In case of supervised classifiers (Parallel Piped & Maximum Likelihood Classifiers) correctness of classification depends on the signature and also reference data.

VII. CONCLUSION

When three different classifiers are compared by analyzing two diverse study areas, it can be concluded that Maximum likelihood classifier (MLC) is superior which has maximum Overall accuracy and Kappa factor. With the obtained result it is evident that the accuracy of supervised classifier is directly dependent on reference data (ground reality). In case, analyst is lacking intimate familiarization with huge, compound and diverse area, unsupervised classification (i.e. K-Means classifier) has a potential to produce satisfactory results.

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