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, Graylevel co-occurrence matrix, Hybrid directional lifting, Local binary pattern.
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Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and
Adaptive Neuro-Fuzzy Inference System
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; waterbody, 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
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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 unsupervised 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
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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
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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
( )is specified as the local window variance of the
value, which ranges from 0.1 to 0.6,
( ) is utilized for
image pixels,
is denoted as the noisy image variance. The
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
,
∑
∑
(
)
, where
,
-
-
(3)
(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.
(
)
(
)
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(
)
386
(6)
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Land Use and Land Cover Classification For Visakhapatnam Using Fuzzy C Means Clustering and
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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)
( ) is added to the smooth region
Then, the estimated minimum direction
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).
∑
∑
‖
‖
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(11)
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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
the Eq. (13).
∑
is calculated by
(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)
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Figure 5 Segmented image after using FCM algorithm (year; 2014)
Figure 6 Segmented image after using FCM algorithm (year; 2017)
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).
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(
(
))
| (
)
(
)|
∑
| (
)
(
)|
(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
( ´ ). The basic rule of ANFIS
is exploited on the extracted feature values ( ´ ) ( ´ )
classifier is determined in the Eq. (19).
(´)
Where,
(´)
(´)
(19)
are denoted as design parameters.
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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
membership functions for the fuzzy sets , and .
is stated as the degree of
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).
(´)
(´)
Layer 3;
Here, every node is a circle node
specified in the Eq. (22).
´
(
( ´ ),
(21)
that evaluates the ratio of rules firing strength, which is
(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
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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,
as false negative, and
is stated as true negative.
is specified
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
Water-body
Vegetation
Settlement
Barren Land
Classification
accuracy
Sensitivity
Specificity
2012
2014
2017
Proposed User’s value Proposed User’s value Proposed User’s value
value
value
value
100%
100%
85%
87.67%
93%
90%
94%
90%
97%
100%
87%
82.5%
96%
100%
89%
67.76%
88%
100%
90%
75%
100%
100%
78%
70%
95%
91.25%
92.75%
88.85%
86.5%
85.625%
93%
98%
87%
73.34%
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87%
92%
392
83%
80%
97%
98%
96%
90%
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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
Water-body
Vegetation
Settlement
Barren Land
Total
2012(ha)
2512.83
3629.62
33893.09
6851468.46
6891504
2014(ha)
2367.09
3597.97
34764.13
6850774.81
6891504
2017(ha)
2301.32
3561.01
36942.98
6848698.69
6891504
2012-2017(ha)
-211.50
-8.41%
-68.61
-1.89%
+3049.89 +8.99%
-2769.77
-0.04%
-
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
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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
Methodology
Maximum likely hood
algorithm [16]
ANFIS
Study area
Visakhapatnam
Classification accuracy (%)
83.35
Visakhapatnam
91.42
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 understanding 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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