Research Article
ISSN 2277–9051
International Journal of Remote Sensing and GIS, Volume 2, Issue 1, 2013, 52-60
© Copyright 2013, All rights reserved Research Publishing Group
www.rpublishing.org
Assessment of Various Block Truncation Coding Based Remote
Sensing Image Classification Techniques
Pravada S. Bharatkar1 and Rahila Patel2
1
Department of Computer Science, RCERT (Nagpur University), Chandrapur, India
Email : [email protected]
2
Department of Computer Science, RCERT (Nagpur University), Chandrapur, India
Email : [email protected]
Received February 10, 2013; received in revised form March 22, 2013; accepted March 23, 2013
Abstract
This work presents a new interactive approach based on block truncation coding (BTC) to classify
various regions in remote sensing (RS) imagery. In the classification, classified maps are the main product. As
such, there exist a several techniques in literature for RS image classification, among which, very few have
proved good precision in classifying RS image. Hence, the thirst of better and faster image classification is
increasing and earned enormous attention. Developing computationally efficient algorithm for image
classification without compromising the classification accuracy is of primary importance. Therefore, we are
experimenting RS image classification by incorporating BTC approach in existing supervised and unsupervised
classification technique. Five themes of land use/land cover classes (LULC) namely, Agricultural land, barren
land, shrubs, waste land with scrub, and water body are considered and assess the accuracy of classification in
terms of overall accuracy and kappa statistics. The results depicted that the supervised classification is superior
to unsupervised classification technique. It is revealed from the present work, the reconstructed RS image by
amalgamating BTC approach in the existing classification techniques can be a novel classification approach to
map LULC in an efficient and accurate way.
Key words: Block Truncation Coding (BTC) approach, remote sensing (RS), classification techniques, land
use/land cover (LULC), supervised classification technique, unsupervised classification technique
1 Introduction
The science and technology of Remote Sensing (RS) has emerged as one of the most fascinating and
challenging subject over the past three decades. RS offers unique capabilities for understanding, monitoring,
forecasting, managing and decision making about our planet’s resources via classification. The goal of
classification is to group image cells into various classes/clusters to prepare classified maps. These maps are
basic source of information for many applications in the field of agriculture, water resources, and environment.
However, classifying RS image into thematic maps remains a challenge due to complexity of the landscape, the
availability of reference data, the selected remotely sensed data, image-processing and image classification
techniques, and the analyst’s experiences. Generally, the content of image such as color, texture and shape and
size plays an important role in semantic image classification. The proper selection of image features and
classification techniques are challenging task of classification, so many researchers delivered different
classification techniques for image classification; such as supervised classification techniques i.e. box or
parallelepiped, minimum distance to mean, mahalanobis and maximum likelihood, etc. and unsupervised
clustering. Each of these techniques has some limitation necessitating for development of computationally
efficient algorithms for image classification without compromising the classification accuracy.
In past, numerous scientists had made efforts in developing various classification approaches for
improving the classification accuracy (Gong and Howarth, 1992; Kontoes et al., 1993; Foody, 1996; Atkinson et
al., 1999; Pal and Mather, 2003; Lu and Weng, 2007; Silakari et al., 2009; Blaschke, 2010). Recently, Kekre et
al. (2012) have made an effort on Block Truncation Coding (BTC) approach for classification of photographic
image of human being, animals, and natural scenery and found a better classification performance. Several
studies also experimented on different aspects with BTC approach by various researchers (Maheswary and
Srivastava, 2009; Silva et al., 2011; Rawat and Patil, 2012 and Samathal and Mohanraj, 2012). Based on the
critical reviews on RS image classification technique by author (Bharatkar and Patel, 2012), the possibility of
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BTC approach in RS image classification is pointed out and they recommended that instead of using all pixel
data of image as feature vector for extracting the information from image, the threshold values of pixels based
on BTC, can be used for better classification of RS image. Therefore, the present work has undertaken to
amalgamate the BTC approach in the existing RS image classification technique to develop a novel
classification approach for mapping LULC in an efficient and accurate way.
2 Proposed Methodology
The present study proposes an evaluation of various RSI classiﬁcation methods to extract the features
of the BTC based reconstructed RS image for effective land use mapping. The RS image and scanned survey of
India (SOI) toposheet were first rectified using the GIS software namely, Integrated Land and Water
Information System (ILWIS) to prepare the base map of the watershed boundary, drainage and road maps of
study area for ground verification. The procured RS image was reconstructed by using BTC approach described
as follows.
2.1 BTC Approach
In BTC approach, first divided the procured RS image into n*n non overlapping blocks of R, G, B
components and computed the interband average image (IBAI); which is the average of all components (R, G,
and B). After this, mean of interband average is taken as threshold (MR, MG, and MB). By using three
independent R, G, and B components of image to calculate three different thresholds and then apply BTC to
each individual R, G and B components. The thresholds be MR, MG, MB can be computed as given below;
MR 
1 m n
 R(i, j )
m  n i1 j 1
MG 
1 m n
 G (i, j )
m  n i 1 j 1
MB 
1 m n
 B(i , j )
m  n i 1 j 1
Now three binary bitmaps are compute as BMr, BMg, BMb. If a pixel in each component (R, G and B)
is greater than or equal to the respective threshold the corresponding pixel position of the bitmap have a value of
1 otherwise it is having the value of 0.
1, if……..R(i, j)>MR
BMr(i, j)={
0,…….if…. R(i, j)<=MR
1, if……..G(i, j)>MG
BMg(i, j)={
0,…….if…. G(i, j)<=MG
1, if……..B(i, j)>MB
BMb(i, j)={
0,…….if…. B(i, j)<=MB
Two mean colors one for the pixel greater than or equal to the threshold and other for the pixels smaller than the
threshold are also calculated .The UM (Upper mean) and LM (Lower mean) can be calculated as;
UR 
m n
1
*   BMr (i, j ) * R (i, j )
 BMr (i, j ) i 1 j 1
UG 
m n
1
*   BMg (i, j ) * G (i, j )
  BMg (i, j ) i 1 j 1
UB 
m n
1
*   BMb(i, j ) * B (i, j )
 BMb(i, j ) i 1 j 1
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Bharatkar and Patel/International Journal of Remote Sensing and GIS, Volume 2, Issue 1, 2013, 52-60
LR 
LG 
LB 
m
1
m * n   BMr (i, j )
n
*  {1  BMr (i, j )} * R(i, j )
i 1 j 1
m n
m *n 1
*  {1  BMg (i, j )} * G (i, j )
m * n    BMg (i, j ) i 1 j 1
m
1
m * n   BMb(i, j )
n
*   {1  BMb(i, j )} * B (i, j )
i 1 j 1
These upper mean and lower mean together form a feature vector or the signature of the newly
reconstructed BTC based RS image as shown in Figure 1.
Figure 1: Spectral representation of original and reconstructed (BTC) RS image in ILWIS
The procured and reconstructed RS images along with scanned SOI toposheet no. 47 J/5 were first
rectified by projecting them into a plane by using map projection. For this purpose, a co-ordinate system was
created with Universal Traverse Mercator (UTM) projection for zone 43 (in which the study area lies), using
Everest (India, 1956) Ellipsoid and Everest (India, Nepal) datum under the GIS image processing utilities of
ILWIS (Integrated Land and Water Information System), GIS (Geographical Information System) software. The
procured and rectified RS images were further interpreted for LULC classification using the various
classification techniques described in the subsequent section. The nationwide classification system prepared by
NRSC, Hyderabad was adopted for LULC mapping (NRSC, 2011). The flow chart shows the complete
procedure after incorporating the BTC approach in the existing algorithm is shown in Figure 2.
The field verification for accuracy assessment, the rectified toposheet was used to delineate the
permanent features in the watershed like roads, rural settlement area, and drainage networks, etc. The base map
of the watershed boundary at 1:50,000 scales were prepared using the location of various contour and drainage
lines. For accuracy assessment, reference data or ground truth data was taken using the same schemes used in
the classification efforts. Since, ground based data is assumed to be 100% correct in accuracy assessments, due
care was taken during the data collection. The classified LULC maps of original and reconstructed images using
various classification methods have been evaluated in terms of some statistical accuracy measures. One basic
accuracy measure is the overall accuracy (OA), which is calculated by dividing the correctly classified pixels
(sum of the values in the main diagonal) by the total number of pixels checked. Besides OA, classification
accuracy of individual classes can be calculated in a similar manner. Since, OA does not indicate how the
accuracy is distributed across the individual categories, more specific accuracies are needed. One may be the
user’ s accuracy (UA) which is the ratio between the number of correctly classified pixels and the classified
totals pixels of particular LULC class, while another may be the producer's accuracy (PA). PA is the ratio
between the number of correctly classified pixels and the reference total pixels. A more appropriate way of
presenting these individual classification accuracies may be in terms of commission error (CE) and omission
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error (OE). Kappa coefficient provides a difference measurement between the observed agreement of two maps
and agreement that is contributed by chance alone. A kappa coefficient above 75% may be interpreted as better
classification than would be expected by random assignment of classes (Susana et al., 2010).
Figure 2: Flow chart of BTC based classification algorithm
2.2 Image Classification Techniques
Image classification technique, which is computer based interpretation of remotely sensed images,
based solely on the detection of the spectral signatures (i.e., spectral response patterns) of various LULC classes.
The computer classification technique of RS images are divided into unsupervised classification and supervised
classification (Pooja et al., 2011; Zhang and Wang, 2012). Unsupervised classification is the process that for
remote sensing images without prior knowledge, only depends to the statistical difference of combination of
different spectroscopic data, and then validates ground objects according to properties of various classified
objects. In unsupervised image classification, no training stage is required, but different algorithms are used for
clustering. Numerous factors affect the classification results, among which important ones being the objective of
classification, the spectral and spatial characteristics of the data, the natural variability of terrain conditions in
geographic region, and the digital classification technique employed (Mishra et al., 2011). In supervised image
classification training stage is required, which means first we need to select some pixels form each class called
training pixels. Find the characteristics of training pixels and also find other pixels which have same
characteristics. The software system is then used to develop a statistical characterization of the reflectance for
each information class (Congalton and Green,1999). This stage is often called signature analysis and may
involve developing a characterization as simple as the mean or the range of reflectance on each band, or as
complex as detailed analyses of the mean, variances and covariances over all bands. Once a statistical
characterization has been achieved for each information class, the image is then classified by examining the
reflectance for each pixel and making a decision about which of the signatures it resembles most. There are
several techniques for making these decisions, called classification techniques as described below.
2.2.1
Clustering
It is an unsupervised classification technique, in which no training stage is required and can used to
determine the natural spectral groupings present in the image. It is a rather quick process in which image data is
grouped into spectral clusters based on the statistical properties of all pixel values. It is an automated
classification approach.
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2.2.2
Box or parallelepiped classifier
It is a very simple supervised classification algorithm. In this, two image bands are used to determine
the training area of the pixels in each band based on maximum and minimum pixel values. Although
parallelepiped is the most accurate of the classification techniques, it is not most widely used. It leaves many
unclassified pixels and also can have overlap between training pixels (Perumal and Bhaskaran, 2010; Xu and
Wei, 2012). This classification is based on simple Boolean “and/or” logic that decides the particular brightness
value in the image belongs to a specified class.
2.2.3
Minimum distance to mean classifier
This is simple in principle based on Euclidean distances towards class means only. For the spectral
values of a pixel to be classified, the distances towards the class means are calculated. If the shortest (Euclidean)
distance to a class mean is smaller than the user-defined threshold, then this class name is assigned to the output
pixel, else the undefined value is assigned.
2.2.4
Mahalanobis distance classifier
This classifier is based on the distances towards class means and the variance-covariance matrix of
each class. For the spectral values of a pixel to be classified, the distances towards the class means are calculated
as Mahalanobis distance (Perumal and Bhaskaran, 2010). The Mahalanobis distance depends on the distances
towards class means and the variance-covariance matrix of each class. The class name with the shortest
Mahalanobis distance is assigned, if this distance is smaller than the user-defined threshold value else, the
undefined value is assigned.
2.2.5
Maximum likelihood classifier (MLC)
It is perhaps the most widely used classification method of classification in remote sensing in which a
pixel with the maximum likelihood is classified into the corresponding class (Xu and Wei, 2012). MLC
algorithm uses Bayes’ rule and a classification method that minimum incorrect probability in terms of statistical
rules. This Classification uses the training data by means of estimating means and variances of the classes,
which are used to estimate probabilities and also consider the variability of brightness values in each class
(Perumal and Bhaskaran, 2010).
3 Study Area and Data Source
The Indian Remote Sensing (IRS) 1D LISS (Linear Imaging Self Scanning) III satellite image of
Ralegaon Siddhi watershed having values in bands G, R, NIR and SWIR with a swath of 141kms in the format
of LGSOWG ((Landsat Ground Station Operators Working Group) of the electromagnetic spectrum with a
spatial resolution have been procured from NRSC (National Remote Sensing Centre), Hyderabad. The dataset
consists of 2282 x 2507 pixels. The advantage of using this dataset is the availability of the referenced date set
(Table 1) produced from field survey, which is used for the classification accuracy purpose.
Table 1: Reference data set
Sr.
No.
1
LULC classes
Total nos. of ground
truth pixels
214
2
shrubs
82
3
Waste land with scrub
111
4
Barren land
59
5
Water body
74
Agriculture Land
Total
540
The study area is a part of Dudh nadi sub-basin in the drought prone region of Parner taluka of Ahmednagar
district of Maharashtra state (India). The watershed is lying between 18054' N to 18057' N and longitudes of
74023' E to 74027' E as shown in Figure 3.
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Figure 3: Location of the study area
4 Result Analysis
In the present work, RS (original and reconstructed) images were processes through image processing
capability of ILWIS GIS to derive an effective LULC map using the various classification techniques. The five
major LULC classes are considered namely, agricultural land, barren land, shrubs, wasteland with scrub and
water body. The confusion matrices for all the supervised and unsupervised classification techniques have been
computed. As the results reflected that supervised maximum likelihood classification (MLC) technique
performed better than other classification methods; the confusion matrix for this classification method is only as
given in Table 2 and classified map is shown in Figure 4.
Table 2: Confusion matrix for supervised maximum likelihood classification technique
LULC class
Agriculture land
Barren land
Shrubs
Waste land with scrub
water body
Column Total
Agriculture land
Barren land
Shrubs
Waste land with scrub
water body
Column Total
Agriculture
land
Barren
Shrubs
Waste land
land
with scrub
Original RS image
214
2
0
47
0
55
0
6
0
0
82
0
0
2
0
58
0
0
0
0
214
59
82
111
Reconstructed RS image (BTC)
214
0
0
50
0
56
0
0
0
0
82
0
0
3
0
61
0
0
0
0
214
59
82
111
57
Water
body
Row
Total
5
0
0
0
69
74
268
61
82
60
69
540
2
0
0
0
72
74
266
56
82
64
72
540
Bharatkar and Patel/International Journal of Remote Sensing and GIS, Volume 2, Issue 1, 2013, 52-60
74024’39’’
74025’48’’
74026’56’’
18054’43’’
18055’14’’
18055’50’’
18056’20’’
74023’31’’
Figure 4: Classified LULC map derived by maximum likelihood classification techniques using reconstructed
(BTC) RS image
Based on the confusion matrix, various statistics such as classification errors (OE and CE) and accuracies (PA
and UA) have been computed for all classification methods. The graphical representation of errors and
accuracies using supervised MLC technique is shown in Figure 5.
Figure 5: Graphical representation of classification errors (E) and accuracies (A) for supervised maximum
likelihood classification technique by using original (O) and reconstructed (R) RS images
The commission error (CE) is also called a misclassification error reflecting overestimation, while the offdiagonal elements omitted during classification is called omission error (OE measure of under estimation). As
stated earlier, the user’s accuracy is ratio between the number of correctly classified and the row total. Since, the
users are concerned about what percentage of the classes has been correctly classified; user’s accuracy reflects
the accurate classification of individual LULC class. The producer's accuracy, which reflects the exact
classification of particular land use/cover class, is the ratio between the number of correctly classified and the
column total. As can be seen from the Figure 5, there is an inverse relationship existed between accuracy and
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Bharatkar and Patel/International Journal of Remote Sensing and GIS, Volume 2, Issue 1, 2013, 52-60
error. The minimum errors (OE and CE) and maximum accuracies (PA and UA) are found for classification of
reconstructed RS image using supervised classification. The accuracies are maximizes with the minimization of
errors from Box to maximum likelihood classifier in proceeding with Mahalanobis and minimum distance to
mean classifiers for mapping most of the LULC classes. The CE for agriculture land and OE for waste land are
comparatively more for all classification methods due the reasons that the misclassification of some of training
pixels of waste land as agricultural land. The shrub is better classified in all classification techniques due to its
distinct features observed in the image. The overall accuracy (OA) representing the sum of all correctly
classified pixels divided by the total number of test pixels and kappa coefficient (difference measurement
between the observed agreement of two maps and agreement that is contributed by chance alone) were also
computed as presented in Table 3 for both original and reconstructed RS image using all classifiers considered
for this study.
Table 3: Overall accuracy and Kappa coefficient for various classification techniques
Name of the
Classification
Technique
Clustering
Box
Minimum distance
Mahalanobis
Maximum likelihood
Original RS Image
Reconstructed RS Image (BTC)
Overall
Kappa
Overall
Kappa
Accuracy (%)
Coefficient
Accuracy (%)
Coefficient
Unsupervised classification technique
60.78
0.477
61.48
0.496
Supervised clustering technique
58.15
0.443
58.70
0.444
81.85
0.762
82.04
0.764
84.26
0.781
85.19
0.794
88.52
0.842
89.81
0.860
It is seen from the Table that the maximum likelihood classifier using reconstructed (BTC) RS image gives
higher overall accuracy of 89.81% with excellent kappa value of 0.860 as compared to other classifiers.
5 Conclusions
The present study focuses on the classification accuracy of the various RS classification techniques.
The procured IRS LISS III image covering Ralegaon Siddhi watershed is reconstructed with novel approach of
BTC and tested for its performance based on overall accuracy and kappa coefficient. The supervised and
unsupervised classification techniques were employed on original and BTC based reconstructed RS image for
their classification into various LULC classes. It is found that, the classification of reconstructed (BTC) RS
image gives minimum errors with maximum accuracies for maximum likelihood supervised classification
method. The supervised classification method is more reliable as compare to unsupervised clustering technique.
The value of overall accuracy of 89.81% and kappa value of 0.860 for classification of reconstructed (BTC) RS
image shows a better performance of maximum likelihood classification technique. Hence, it can be concluded
from the study that the incorporation of BTC approach in the existing algorithm improves the overall
classification accuracy by minimizing the errors.
References
Aplin, P., Atkinson, P. M. and Curran, P. J.(1999). Per-field classification of land use using the forthcoming
very fine spatial resolution satellite sensors: problems and potential solutions. In P.M. Atkinson and
N.J. Tate (Eds.), Advan. in Remote Sensing and GIS Analysis, New York: John Wiley and Sons., 219–
239.
Bharatkar, P. S., Patel, R. (2012). A survey on RSI classification techniques. International Journal of Advanced
Research in Computer Science (ISSN No. 0976-5697), 3(7), 218-223.
Blaschke, T. (2010). Object based image analysis for remote sensing.
Photogrammetry and Remote Sensing, 65(1), 2-16.
ISPRS International Journal of
Congalton, R. G. and Green, K.(1999). Assessing the Accuracy of Remotely Sensed Data, Principles and
practices (Boca Raton, London, New York: Lewis Publishers).
59
Bharatkar and Patel/International Journal of Remote Sensing and GIS, Volume 2, Issue 1, 2013, 52-60
Foody, G. M. (1996). Approaches for the production and evaluation of fuzzy land cover classification from
remotely-sensed data. International Journal of Remote Sensing, 17, 1317–1340.
Gong, P., Howarth, P. J. (1992). Frequency-based contextual classification and gray-level vector reduction for
land-use identification. Photogrammetric Engineering and Remote Sensing, 58, 423–437.
Kekre, H. B., Thepade, Sudeep , Das, R. K. and Ghosh, S. (2012). Image classification using block truncation
coding with assorted colour spaces. International Journal of Computer Application, 44(6), 9-14.
Kontoes, C., Wilkinson, G. G., Burrill, A. and Goffredo, S., and Megier, J. (1993). An experimental system for
the integration of GIS data in knowledge-based image analysis for remote sensing of agriculture.
International Journal of Geographical Information System, 7, 247–262.
Lu, D. and Weng, Q.(2007). A survey of image classification methods and techniques for improving
classification performance. International Journal of Remote Sensing, 28(5), 823-870.
Maheswary, Priti, Srivastava, Namita (2009). Retrieval of remote sensing images using colour & texture
attribute. International Journal Computer Science and Information Security, 4(1 & 2).
Mishra, Jayant, Sharma, Anubhav and Chaturvedi, Kapil (2011). An unsupervised cluster-based image retrieval
algorithm using relevance feedback. International Journal of Management and Information Technology,
3(2),9-16.
NRSC (2011). Natural Resource Census - Land Use Land Cover Database. Technical Report – Ver.1, National
Remote Sensing Centre, NRSC-RS&GISAA-LRUMG-LUCMD-N0V.,2011-TR-316
Pal, M. and Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover
classification. Remote Sensing Environment, 86, 554–565.
Perumal, K. and Bhaskaran, R. (2010). Supervised classification performance of Multispectral images. Journal
of Computing, 2 (2), 124-128.
Pooja, A. P., Jayanth, J. and Koliwad, Shivaprakash (2011). Classification of RS data using decision tree
approach.International Journal of Computer Applications (0975 – 8887), 23(3),7-11.
Rawat, Sunita and Patil, Dharmaraj (2012). Content based image retrieval using block truncation coding. World
Journal of Science and Technology, 2(3), 34-37.
Samathal, S., Mohanraj, N. (2012). BTC with K means classifier using color image clustering. Journal of
Computer Application, 5(EICA).
Silakari, Sanjay, Motwani, Mahesh and Maheshwari, Manish (2009). Color image clustering using block
truncation algorithm, International Journal of Computer Science, 4(2), 31-35.
Silvia, K. Sneha, Vamsidhar, Y. and Sudhakar, G. (2011). Colour image clustering using K-Means. International
Journal of Computer Science and Technology, 2(1), 11-13.
Susana, M. Vieira, Uzay, Kaymak Joao, and Sousa M. C. (2010). Cohen Kappa Coefficient as a performance
Measure for Feature Selection”, Proceedings IEEE International Conference, 2010, doi: 978-1-42448126-2/10.
Xu, Mengxi and Wei, Chenglin. (2012). Remotely sensed image classification by complex network eigenvalue
and connected degree. Hindawi Publishing Corporation Computational and Mathematical Methods in
Medicine. doi:10.1155/2012/632703.
Zhang, Hebing, and Wang, Shidong (2012). Research progress of computer automatic classification technology
and methods based on remote sensing images, The 2nd Intern. Conf. Comp. Appl. Sys. Model.
Published by Atlantis Press,Paris, France.
60