LAND USE AND LAND COVER CLASSIFICATION FOR...

http://www.iaeme.com/IJMET/index.asp 382 [email protected]

International Journal of Civil Engineering and Technology (IJCIET)

Volume 10, Issue 1, January 2019, pp.382–395, Article ID: IJCIET_10_01_036

Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

©IAEME Publication Scopus Indexed

LAND USE AND LAND COVER

CLASSIFICATION FOR VISAKHAPATNAM

USING FUZZY C MEANS CLUSTERING AND

ADAPTIVE NEURO-FUZZY INFERENCE

SYSTEM

Dr. Ch. Kannam Naidu

Civil Engineering Department, Vignan’s Institute of Information Technology (VIIT),

Visakhapatnam-530049, Andhra Pradesh, India

Dr. Ch. Vasudeva Rao

Civil Engineering Department, Aditya Institute of Information Technology (AITAM),

Tekkali, Srikakulam-532201, Andhra Pradesh, India

Dr. T. V. Madhusudhana Rao

Department of Computer Science Engineering, Vignan’s Institute of Information Technology

(VIIT), Visakhapatnam-530049, Andhra Pradesh, India

ABSTRACT

In current decades, Land Use (LU) and Land Cover (LC) classification is the most

challenging research area in the field of remote sensing. This research helps in

understanding the environmental changes for ensuring the sustainable development.

In this research, LU and LC classification assessed for Visakhapatnam city. After

collecting the satellite images, Hybrid Directional Lifting (HDL) technique was used

to remove the saturation and blooming effects in the input images. The pre-processed

satellite images were used for segmentation by applying Fuzzy C means (FCM)

clustering. Then, Local Binary Pattern (LBP) and Gray-level co-occurrence matrix

(GLCM) features were utilized to extract the features from the segmented satellite

images. After obtaining the feature information, a multi-class classifier: Adaptive

Neuro-Fuzzy Inference System (ANFIS) was used to classify the LU and LC classes;

water-body, vegetation, settlement, and barren land. The experimental outcome

showed that the proposed system effectively distinguishes the LU and LC classes by

means of sensitivity, specificity, and classification accuracy. The proposed system

enhances the classification accuracy up to 7% compared to the existing systems.

Key words: Adaptive Neuro-fuzzy inference system, Fuzzy C means clustering, Gray-

level co-occurrence matrix, Hybrid directional lifting, Local binary pattern.

Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and

Adaptive Neuro-Fuzzy Inference System

http://www.iaeme.com/IJCIET/index.asp 383 [email protected]

Cite this Article: Dr. Ch. Kannam Naidu, Dr. Ch. Vasudeva Rao and Dr. T. V.

Madhusudhana Rao, Land Use and Land Cover Classification For Visakhapatnam

Using Fuzzy C Means Clustering and Adaptive Neuro-Fuzzy Inference System,

International Journal of Civil Engineering and Technology (IJCIET), 10 (1), 2019, pp.

382–395.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1

1. INTRODUCTION

In present scenario, LU and LC classification using remote sensing image plays an essential

role in numerous applications like biological resources (fragmentation, wetlands, and habitat

quality), agricultural practice (riparian zone buffers, conservation easements, cropping

patterns, and nutrient management), land use planning (suburban sprawl, growth trends,

policy regulations and incentives) [1-2], and forest management (resource-inventory,

harvesting, health, stand-quality, and reforestation) [3-4]. Generally, the remote sensing

images delivers large scale and up-to date information about the earth surface condition. The

present remote sensing image has two major issues; maintaining the large volume of data and

noise associated with the image [5-6]. To address these issues, numerous methodologies are

developed by the researchers such as, artificial neural network [7], support vector machine

[8], hybrid classification [9], extreme gradient boosting classifier [10], etc. The conventional

methods in LU and LC classification are extremely affected by the environmental changes

like haphazard, uncontrolled urban development, destruction of essential wetlands, loss of

prime agricultural lands, deteriorating environmental quality, etc.

To address these concerns and also to enhance the LU and LC classification, a new

supervised system was developed in this research. Here, the satellite images were collected

for Visakhapatnam city in three different time periods: 2012, 2014, and 2017. The unwanted

noises, saturation and blooming effects in the collected satellite images were eliminated by

using HDL pre-processing technique. Additionally, the HDL technique retains the essential

details and also to improve the visual appearance of the images. The respective pre-processed

satellite images were used for segmentation by employing FCM clustering. The major

advantage of FCM clustering was very robust to clustering parameters that help to decrease

the computational charges. Then, hybrid feature extraction was carried-out to extract the

features from the segmented images. The hybrid feature extraction comprises of LBP and

GLCM (Homogeneity and energy)) features, which were utilized to obtain the feature subsets

from the set of data inputs by the rejection of redundant and irrelevant features. These feature

values were given as the input for ANFIS classifier to classify the LU and LC classes; water-

body, vegetation, settlement, and barren land.

This research paper is structured as follows. Section 2 denotes a broad survey of recent

papers in LU and LC classification. In section 3, an effective supervised system is developed

for LU and LC classification. In section 4, comparative and quantitative evaluation of

proposed and existing systems are presented. The conclusion is done in the 5.

2. LITERATURE REVIEW

Several new systems are developed by the researchers in LU and LC classification. In this

section, the evaluation of a few essential contributions to the existing literature papers are

presented.

Usually, automatic LU and LC classification helps the policy makers to understand the

environmental changes for ensuring the sustainable development. Hence, LU and LC feature

identification and classification have emerged as an essential research area in the field of

remote sensing. S. Sinha, L.K. Sharma, and M.S. Nathawat, [11] utilized maximum likelihood

Dr. Ch. Kannam Naidu, Dr. Ch. Vasudeva Rao and Dr. T. V. Madhusudhana Rao


classifier for LU and LC classification. This research improved the classification accuracy

with the combined use spectral and thermal information from the satellite imageries. The

developed classification approach was only suitable for a minimum number of classes not for

maximum number of classes.

B. Gong, J. Im, and G. Mountrakis, [12] developed an effective optimized classification

algorithm: artificial immune network for LU and LC classification. The developed algorithm

helps in preserving the best anti-bodies of every LU and LC classes from ant-body population

suppression and also the mutation rates were self-adaptive on the basis of model performance

between training generations. In this research study, the spectral angle mapping distance and

Euclidean distance were used for measuring the affinity between the feature vectors. Finally,

genetic algorithm-based optimization was applied for better discriminate between the LU and

LC classes with similar properties. A major concern in the developed optimized classification

algorithm was high computational time, which was quite high compared to the other systems

in LU and LC classification.

A.K. Thakkar, V.R. Desai, A. Patel, and M.B. Potdar, [13] presented a new system for LU

and LC classification. The main goal of this research study was to extract the best LU and LC

information for Gujarat region (India) in dissimilar time periods: 2001, and 2011. In this

research study, the maximum likelihood classifier was used to IRS LISS-III imagery of 2011

and 2001 for classifying the LU and LC classes: agricultural land, prosopis or scrub forest,

built-up area, water-body, forest, barren land, river sand, and quarry. At last, a new

framework (normalized difference water index and drainage network) was applied for post

classification corrections. Here, a pre-processing method was required for further enhancing

the LU and LC classification.

H. Zhang, J. Li, T. Wang, H. Lin, Z. Zheng, Y. Li, and Y. Lu, [14] developed a new

approach for combining the synthetic aperture and optical radar data (radar SPOT-5 data) for

improving the LU and LC classes. In this literature paper, principle component analysis, local

linear embedding and ISOMAP were employed with three dissimilar synthetic and optical

apertures. In the experimental phase, the developed approach performance was evaluated by

means of classification accuracy and kappa co-efficient. In a large sized satellite image

dataset, the developed approach failed to accomplish better LU and LC classification.

Q. Chen, G. Kuang, J. Li, L. Sui, and D. Li, [15] presented a new un-supervised system

for LU and LC classification on the basis of polarimetric scattering similarity. The developed

system includes minor and major scattering mechanisms, which were identified automatically

based on the multiple scattering similarity magnitudes. Additionally, the canonical scattering

corresponds to the maximum scattering similarity, which was observed as the main scattering

mechanism. The obtained result using jet propulsion laboratory’s AIRSAR L-band PolSAR,

national aeronautics, space administration imagery exposes that the developed approach was

more effective related to other existing systems. In this literature study, the developed un-

supervised system did not focus on the segmentation that was considered as one of the major

concerns.

To overcome the above mentioned problems, an effective supervised system was

developed for improving the performance of LU and LC classification.

3. PROPOSED SYSTEM

Urbanization growth is a process of changing rural life-style into urban ones, which is

characterized as the progressions that happen in the territorial and socio-economic progress of

a zone, including the general changes of LU and LC classification from being non-developed

to develop. Here, it is essential to analyze and study the drastic changes happened due to

global urbanization periodically. In this research study, a new system was proposed to analyze




the urbanization changes occurred in Visakhapatnam city. The proposed system comprises of

five phases: image collection, pre-processing of collected image, segmentation, feature

extraction and classification. The work flow of proposed system is denoted in the Fig. 1.

Figure 1 Work flow of proposed system

3.1. Image collection

The satellite image utilized for LU and LC classification was collected from the 1.5km spatial

resolution of SEA Wi-FS data. Here, Visakhapatnam city is considered as a study area, which

is located at 17.686815 of latitude and 83.218483 of longitude and nearer to the Coromandel

Coast of the Bay of Bengal. The satellite image of Visakhapatnam city is collected for three

years; 2012, 2014, and 2017. The sample collected satellite image is denoted in the Fig. 2.

Figure 2 Sample image of Visakhapatnam city (year; 2017)

3.2. Pre-processing using HDL approach

The HDL approach varies from the traditional pre-processing approaches in orientation

evaluation and pixel classification. In satellite image denoising, the HDL approach comprises

of three important phases; pixel classification, orientation estimation and hybrid transform.

The image pixel classification results into the pixels belonging to two groups namely; smooth

and texture regions. Here, the orientation evaluation is performed on the basis of pixel



correlation and classification. Finally, hybrid transform accomplishes the transform on pixel

level instead of block-based transform for avoiding artifacts in the smooth regions.

3.2.1. Image pixel classification

The input satellite image has two regions; texture and smooth region. In this technique, the

smooth and texture regions are set by using the flag and threshold values. A flag value (zero)

represents a smooth region and (one) represents a texture region. Hence, a flag value indicates

the local activity of every pixel in the satellite image. In this pre-processing technique, two

classification phases are performed in order to accomplish pixel classification. At first, the

satellite image is sub-categorized into sub-blocks and these sub-blocks are further

classified into Region of Non-Interest (RONI) and region of interest (ROI). Secondly, the

pixel classification process is performed on every pixel of ROI instead of sub-blocks. The

collected satellite image is sub-classified into smooth and texture regions by using the Eq. (1)

and (2).

( )

( ) ( ) (1)

( )

( ) ( ) (2)

Where, ( ) is represented as the satellite image pixels, is denoted as the threshold

value, which ranges from 0.1 to 0.6, ( )is specified as the local window variance of the

image pixels, is denoted as the noisy image variance. The ( ) is utilized for

separating the noisy image into smooth and texture regions on the basis of threshold .

3.2.2. Direction evaluation

The precision of direction evaluation is the key factor for obtaining good denoising

performance. Initially, the gradient factors and are considered and then the convolution

of a satellite image along with the gradient factors are calculated for estimating the

orientation, which is mathematically represented in the Eq. (3) and (4).

∑ ∑ ( )

, where ,

- (3)

∑ ∑ ( )

, where ,

- (4)

Where, and are denoted as the size of a satellite image and and

are represented as the new convolution matrices. At last, the direction information of

image pixel is evaluated by using the Eq. (5).

( ) ( ) (5)

3.2.3. Direction modification

In the ROI blocks, image pixels are further sub-divided into two types; pixels belong to the

smooth regions and pixels belongs to the image edges in order to modify the direction of

every pixel that is mathematically given in the Eq. (6). Though, it is very hard to calculate the

directional transform of pixels in the smooth regions.

( ) ( ) ( ) (6)




3.2.4. Hybrid transform

In this sub-section, the HDL technique utilizes a pixel based classified image ( ), which is

the resultant image of Bayesian classification method. Then, the minimum direction

estimation is obtained by utilizing ( ) and directional information of the satellite image

( ). The main aim of hybrid transform is to diminish the noise occurred in the

smooth image. The minimum direction estimation is calculated by using the Eq. (7) and (8).

( ) ( ) , ( ) ( )- (7)

( ) ( ) (8)

Then, the estimated minimum direction ( ) is added to the smooth region

of the satellite image in order to obtain the hybrid transform ( ), which is represented in

the Eq. (9).

( ) ( ) ( ) (9)

The computed hybrid value is subtracted from the small random value matrix ( ) that

ranges from 0 to 3. Finally, the denoised satellite image ( ) is obtained by using the Eq.

(10).

( ) ( ) ( ) (10)

Where, ( ) is represented as the small random number matrix. Fig. 3 represents the

pre-processed image after applying HDL technique.

Figure 3 Sample pre-processed image after applying HDL technique (year; 2017)

3.3. Segmentation using FCM algorithm

After pre-processing the input satellite image, FCM algorithm is used for segmenting the LU

and LC classes from a satellite image. In existing segmentation algorithms, it is hard to

segment the ill-defined portions that greatly decreases the segmentation accuracy. To address

this concern, FCM algorithm is used in this research for localizing the object in complex

template. Generally, FCM adopts fuzzy set theory for assigning a data object to more than one

cluster. The FCM clustering considers every object as a member of each cluster with a

variable degree of “membership” function. The similarity between the objects are evaluated

by using a distance measure that plays a crucial role in obtaining correct clusters. In each and

every iteration of FCM algorithm, the objective function is minimized that is mathematically

given in the Eq. (11).

∑ ∑ ‖ ‖

(11)



Where, is represented as clusters, is denoted as data points, is stated as degree of

membership for the data point in cluster , and is represented as the centre vector of

cluster . The norm ‖ ‖ calculates the similarity of the data points to the centre vector

of cluster . For a given data , the degree of membership is calculated by using the Eq.

(12).

∑ (‖ ‖

‖ ‖)

(12)

Where, is denoted as the fuzziness coefficient and the center vector is calculated by

the Eq. (13).

∑

∑

(13)

In the Eq. (12) and (13), the fuzziness coefficient calculates the tolerance of the

clustering. The higher value of represents the larger overlap between the clusters. In

addition, the higher fuzziness coefficient utilizes a larger number of data points, where the

degree of membership is either one or zero. The degree of membership function evaluates the

iterations completed by the FCM algorithm. In this research study, the accuracy is measured

by using the degree of membership from one iteration to the next iteration , which is

calculated by the Eq. (14).

|

| (14)

Where, is represented as the largest vector value, and

are denoted as the degree

of membership of iterations and . The segmented LU and LC areas are graphically

denoted in the figures 4, 5 and 6. After segmentation, feature extraction is carried out for

extracting the feature vectors from the segmented regions.

Figure 4 Segmented image after using FCM algorithm (year; 2012)






3.4. Extracting the features from segmented satellite images

The feature extraction is defined as the action of mapping a satellite image from image space

to the feature space that converts large redundant data into a reduced data representation. In

this research study, feature extraction is performed on the basis of LBP and GLCM features.

The detailed description about the feature descriptors are given below.

3.4.1. Local binary pattern

The LBP is a texture analysis descriptor that converts a segmented satellite image into labels

based on the luminance value. Here, gray-scale invariance is an essential factor, which

depends on the local and texture patterns of a segmented image. In a satellite image , the pixel

position and radius are represented as , which are derived by using the central pixel

value of as the threshold to signify the neighbourhood pixel value . Further, the pixel

binary value is weighted using the power of two and then summed to produce a decimal

number for storing in the location of central pixel that is mathematically given in the Eq.

(15).

( ) ∑ ( ) ( ) *

+ (15)

Where, is represented as the gray level value of the central pixel of a local

neighbourhood. The basic neighbourhood LBP model is (p-neighbourhood), which gives

output that leads to a large number of possible patterns. The uniform model of LBP is

accomplished only when the jumping time is maximized. It is measured by using the Eq. (16).



( ( )) | ( ) ( )| ∑ | ( ) ( )| (16)

Where, is represented as the maximum jumping time.

3.4.2. Gray-level co-occurrence matrix

In addition, a high level feature named as GLCM is employed for extracting the features of

segmented satellite image in order to differentiate the LU and LC areas. GLCM is the most

recognized texture analysis descriptor, which calculates the image characteristics associated

with second order statistics. The GLCM descriptor comprises of twenty-one features, in that

energy and homogeneity are considered in this research work for extracting the features from

a segmented satellite image. After extracting the feature vectors using LBP and GLCM

features. The obtained feature information is given as the input for an appropriate classifier:

ANFIS in order to perform classification.

3.4.2.1. Energy

The energy calculates the uniformity of normalized pixel pair distributions and also

determines the number of repeated pairs. Here, energy feature has a normalized value with the

maximum range of one. The higher energy value occurs only when the gray level distribution

has a periodic or constant form. Energy helps to reflect the depth and smoothness of the

satellite image texture structure. The formula to calculate the energy is given in the Eq. (17).

∑ ∑ ( )

(17)

3.4.2.2. Homogeneity

Homogeneity determines the closeness of distribution elements in the gray level matrix. To

quantitatively characterize the homogeneous texture regions for similarity, the local spatial

statistics of the texture is calculated using scale and orientation selective of Gabor filtering.

The segmented satellite image is subdivided into a set of homogeneous texture regions, then

the texture features are related to the regions of indexed image data. In GLCM, homogeneity

calculates four directions (i.e. = 0◦, 45◦, 90◦ or 135◦) with a feature vector size of four.

Homogeneity delivers high accuracy of detection in the defected areas, which are described

by a weak variation in grey level. The formula to calculate the homogeneity is represented in

the Eq. (18).

∑ ∑

( )

(18)

Where, is represented as the number of gray levels of satellite image, ( ) is denoted

as the pixel value of the position ( ) and is represented as the normalized co-occurrence

matrix.

3.5 Classification using ANFIS classifier

After obtaining the feature values, ANFIS classifier is used for classifying the patterns of a

satellite image. In this research, ANFIS classifier accomplishes multiple targets, because it is

more feasible and reliable compared to the individual target. ANFIS is a neuro-fuzzy model

that has the advantage of both neural networks and fuzzy logic. Initially, the learning process

is exploited on the extracted feature values ( ́) ( ́) ( ́). The basic rule of ANFIS

classifier is determined in the Eq. (19).

( ́) ( ́) ( ́) (19)

Where, are denoted as design parameters.




Layer 1;

In layer 1, every node is a square node with a node function. These node functions are

selected from the bell shaped curve with minimum 0 and maximum 1 value, which is given in

the Eq. (20).

( ́) ( ́) ( ́)

( ́)

,(( ) ⁄ ) - (20)

Where, are denoted as the parameter set and is stated as the degree of

membership functions for the fuzzy sets , and .

Layer 2;

In layer 2, every node is a circle node ∏ that multiplies the incoming values and send the

product out, which is mathematically represented in the Eq. (21).

( ́) ( ́) ( ́), (21)

Layer 3;

Here, every node is a circle node that evaluates the ratio of rules firing strength, which is

specified in the Eq. (22).

´

( ) (22)

Layer 4;

In layer 4, every node is a square node with a node function that is denoted in the Eq. (23).

´ (23)

Where, is represented as the output of layer 3.

Layer 5;

In this layer, all the incoming values are summarized and the overall output values are

denoted in the Eq. (24) and (25).

∑ ´ ∑ ´

∑ ´ (24)

´ ´ (25)

4. EXPERIMENTAL RESULT AND DISCUSSION

In the experimental phase, the proposed system was simulated by using MATLAB (version

2018a) with 3.0 GHZ-Intel i5 processor, 1TB hard disc, and 8 GB RAM. The proposed

system performance was related to other existing systems (Maximum likely hood algorithm

[16]) for estimating the effectiveness and efficiency of the proposed system. The proposed

system performance was validated by means of classification accuracy, sensitivity, and

specificity.

4.1. Performance measure

Performance measure is defined as the regular measurement of outcomes that develops a

reliable information about the efficiency and effectiveness of the proposed system. Also,

performance measure is the process of analyzing, collecting, and reporting information about



the performance of a group or individual. The mathematical formula of accuracy, sensitivity,

and specificity are denoted in the Eq. (26), (27), and (28).

(26)

(27)

(28)

Where, is denoted as false positive, is represented as true positive, is specified

as false negative, and is stated as true negative.

4.2 Quantitative analysis

In this segment, the LU and LC map was related to the reference data in order to calculate the

classification accuracy, sensitivity and specificity of the proposed system. In this research

paper, the reference data was prepared by considering the sample points of Google earth. The

obtained ground truth data helps in verifying the classification accuracy, sensitivity and

specificity of the proposed system. Here, the overall classification accuracy of proposed

system for the years of 2012, 2014 and 2017 are 95%, 92.75%, and 86.5%. Similarly, the

overall sensitivity of proposed system for the years of 2012, 2014 and 2017 are 93%, 87%,

and 97%. Correspondingly, the overall specificity of proposed system for the years of 2012,

2014 and 2017 are 98%, 92%, and 98%. The user’s value attains minimum specificity,

sensitivity and classification accuracy value, compared to the proposed system. The results of

the classification accuracy, sensitivity and specificity assessments are presented in the table 1.

The graphical comparison of accuracy, sensitivity and specificity are represented in the Fig. 7.

Table 1 Proposed system performance assessment report

LU and LC

classes

2012 2014 2017

Proposed

value

User’s value Proposed

value

User’s value Proposed

value

User’s value

Water-body 100% 100% 85% 87.67% 93% 90%

Vegetation 94% 90% 97% 100% 87% 82.5%

Settlement 96% 100% 89% 67.76% 88% 100%

Barren Land 90% 75% 100% 100% 78% 70%

Classification

accuracy

95% 91.25% 92.75% 88.85% 86.5% 85.625%

Sensitivity 93% 87% 87% 83% 97% 96%

Specificity 98% 73.34% 92% 80% 98% 90%




Figure 7 Graphical comparison of classification accuracy, sensitivity and specificity

From the analysis of table 2, the settlement regions in Visakhapatnam city are increased

up to 8.99% from the year of 2012-1017, and the vegetation and water-body regions are

decreased up-to 1.89% and 8.41%. Hence, the increase in industrial areas and merchant

establishments are playing a major role in loss of agriculture areas. It is evaluated that the

Eutrophication phenomena are taking place in all the lakes and small water bodies, which

disappeared due to the indiscriminate dumping of solid waste and deposition of sediments in

Visakhapatnam city.

Table 2 Analysis report of Visakhapatnam city in terms of hectares (ha)

LU and LC classes 2012(ha) 2014(ha) 2017(ha) 2012-2017(ha)

Water-body 2512.83 2367.09 2301.32 -211.50 -8.41%

Vegetation 3629.62 3597.97 3561.01 -68.61 -1.89%

Settlement 33893.09 34764.13 36942.98 +3049.89 +8.99%

Barren Land 6851468.46 6850774.81 6848698.69 -2769.77 -0.04%

Total 6891504 6891504 6891504 - -

4.3. Comparative analysis

The comparative analysis of proposed and existing system is detailed in the table 3. M.

Harika, S.K. Aspiya Begum, S. Yamini, and K. Balakrishna, [16] developed an effective

system for LU and LC classification. In this research study, the satellite images were collected

for Visakhapatnam city in different time periods; 1988 and 2009. Then, histogram

equalization was performed on each image to improve the quality of the collected satellite

image. At last, maximum likely hood classifier was used for classifying the LU and LC

classes; built-up area, agricultural land, water bodies, barren area and shrubs. The developed

system almost achieved 83.35% of classification accuracy.

However, the proposed system achieved 91.42% of classification accuracy, which was

higher compared to the existing paper. In this research, the proposed system: FCM based

ANFIS algorithm extracts the both linear and non-linear characteristics of the satellite image

and also preserves the quantitative relationship between the extracted feature values. The

performance measures confirm that the proposed system performs effectively in LU and LC



classification in light of classification accuracy, sensitivity, and specificity. The efficiency

and effectiveness of the proposed system are represented in the tables 1 and 3.

Table 3 Comparative analysis of proposed and existing system

5. CONCLUSION

The main goal of this research work is to provide an effective supervised system for

classifying the LU and LC classes. The proposed system helps the research analysts in under-

standing the environmental changes for ensuring the sustainable development, especially for

Visakhapatnam city. In this scenario, HDL technique was applied to remove the saturation

and blooming effects in the input satellite images. The denoised satellite images were given as

the input for FCM clustering for segmenting the LU and LC areas. Then, hybrid feature

extraction (LBP and GLCM (Homogeneity and energy)) was employed for extracting the

feature values. These feature values were classified by using the classifier: ANFIS. Compared

to other existing systems in LU and LC classification, the proposed system achieved a

superior performance, which showed 7% of improvement in classification accuracy. In future

work, a new unsupervised system was developed for analyzing the LU and LC classes for

other metropolitan cities in India.

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