Classification of Satellite broadcasting Image and Validation Exhausting Geometric Interpretation

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613

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Classification of Satellite broadcasting Image and Validation Exhausting

Geometric Interpretation M. Srinivasa Rao

1 Kartheek V. L

2 Dr. T. Madhu

3

1,2Assistant Professor

3Principal

1Department of Computer Science Engineering

1,2,3Swarnandhra 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].

1) Manual classification

2) Automatic classification

3) Hybrid classification

Classification of Satellite broadcasting Image and Validation Exhausting Geometric Interpretation

(IJSRD/Vol. 3/Issue 10/2015/012)


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:

Using supervised classification (see Figure 3) satellite

images are classified by using a known input called training

data from an analyst. An image analyst selects a

representative sample sites with known class types (i.e.

training sample or training signature) and compares the

spectral properties of each pixel in the image with those of

the training samples, then labels the pixel to the class type

according to decision rules[11]. Large number of supervised

classification methods have been developed and hierarchy of

Satellite image classification techniques is shown (See

Figure 2).




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.

Satellite Image

Geo-Spatial DataBase

Raster Data Pre-Processing

Sample Vector Data shape File

Classification

Generate Signature File

Training Data

Pixel Data

Classified Image

Confusion Matrix

Validation

Classification Schema

Stop

Start




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:

Parallel piped classifier [39] divide each axis of multi

spectral attribute space in to assessment regions called

classes on the basis of its range (i.e. lowest and highest

values) of pixels. The correctness of the classifier depends

on the range in consideration of population statistics of each

class (see Figure 8).




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 = (

)

(

) (7)

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:

LISS-IV satellite image is a multispectral spatial data with

three bands (B2, B3, B4) and 5.8 M spatial resolution. To

demonstrate the capabilities of different classification

techniques, two different study areas from LISS-IV satellite

image of approximately 3 x 1.5 Km2 covers rectangle is

located in Eluru city, AP, India is selected. These areas are

having various land covers like Urban area, Aqua ponds,

Agriculture, Sandy area etc. Study Area-I located between

160 42' 33.10" N 810 05'43.61" E and 160 41' 30.74" N 810

08' 46.70"E (see Figure 10) covers the Urban and its




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

Cla

ssif

ied

dat

a C1 N11 N12 N13 N14 N15 N16 N1+

C2 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.

Overall accuracy is the ration between the total

number of correctly classified pixels (Diagonal elements of

confusion matrix) and total number of reference pixels. k




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

Cla

ssif

ied

dat

a 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

Area 66.66%

70.00%




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



Cla

ssif

ied

dat

a 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


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



Cla

ssif

ied

dat

a 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


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

classifiers are applied on Study Area-II which covers

different set of patterns. Firstly, K-Means algorithm is

applied with k=6 and the classification results (see Figure

18) shows Yellow: Creek, Blue: Aqua Ponds, Pink with

Dots: Sandy area, Light green: Grass Area, Red: Trees,

Green: Agriculture.




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

Cla

ssif

ied

dat

a 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

Table 4: Confusion Matrix And Kappa Factor To Validate Classes Of Study Area-II Using K-Means Classifier




Reference Data

(Ground truth)

Row Total

C Q S G T A

Cla

ssif

ied

dat

a 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


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

Cla

ssif

ied

dat

a

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


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

were assessed by accuracy measures and they can be found

in Table-7 for study area-1 and study area-II. In this study,

reference data (see Figure-12 & Figure-13) is taken from the

field experts and signature file is generated with 98%

accuracy using visual interpretation.




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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