International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
TEXTURE ANALYSIS FOR CLASSIFICATION OF RISAT-II IMAGES
D. Chakraborty?', S Thakur®, A Jeyaram®, YVN Krishna Murthy^, VK Dadhwal*
" Regional Remote Sensing Centre — East, NRSC, ISRO, Kolkata, India (deba.isro@gmail.com)
? Sikkim Manipal Institute of Technology, Sikkim, India
"NRSC, ISRO, Hyderabad, India
Commission III, Working Group ICWG III/VII
KEYWORDS: Image, Texture, Method, Interpretation, Analysis, Development and Classification
ABSTRACT
RISAT-II or Radar Imaging satellite - II is a microwave-imaging satellite lunched by ISRO to take images of the earth during day
and night as well as all weather condition. This satellite enhances the ISRO's capability for disaster management application together
with forestry, agricultural, urban and oceanographic applications. The conventional pixel based classification technique cannot
classify these type of images since it do not take into account the texture information of the image. This paper presents a method to
classify the high-resolution RISAT-II microwave images based on texture analysis. It suppress the speckle noise from the microwave
image before analysis the texture of the image since speckle is essentially a form of noise, which degrades the quality of an image;
make interpretation (visual or digital) more difficult. A local adaptive median filter is developed that uses local statistics to detect the
speckle noise of microwave image and to replace it with a local median value. Local Binary Pattern (LBP) operator is proposed to
measure the texture around each pixel of the speckle suppressed microwave image. It considers a series of circles (2D) centered on
the pixel with incremental radius values and the intersected pixels on the perimeter of the circles of radius r (where r = 1, 3 and 5) are
used for measuring the LBP of the center pixel. The significance of LBP is that it measure the texture around each pixel of the image
and computationally simple. ISODATA method is used to cluster the transformed LBP image. The proposed method adequately
classifies RISAT-II X band microwave images without human intervention.
1.0 INTRODUCTION
Optical remote sensing technology is used for mapping the
earth surface. But it cannot map the earth surface in all weather
conditions since optical bands cannot penetrate clouds, smog
and haze. Microwave remote sensing technology is used as an
alternative technology for mapping the earth surface features
especially when optical data is not available (Oliver et al.,
1998). But interpretation of microwave images is very difficult
due to the presence of texture in these images. Classical
methods namely K-Means (Hartigan et al. 1979), Fuzzy C
Means (Bezdek et al. 1984) and methods of Minimum Distance
(Richards, 1995) are not adapted because they fail to take into
account the fact that a texture can be represented as a mix of
smooth regions and very sharp transitions. The issue of texture
based image classification is an old and difficult problem,
which is still a field of a lot of research.
Relevant studies on Texture Classification: GLCM (Gray Level
Co-occurrence matrix) algorithm (Tsai et al. 2006; Clausi et al.
2004; Tsai et al. 2005; Haralick et al. 1973) quantifies the
texture by measuring the spatial frequency of co-occurrence of
pixel gray levels in a user defined moving kernel (window).
Since the size of a GLCM matrix depends on the data range of
pixel gray values, images of large numbers of data bits may
result in large matrix sizes during GLCM operation and require
a large amount of memory and CPU cycles to handle the
computation. The reductions of GLCM matrix sizes by
rescaling the image gray levels to a lower data bit number
reduce the classification accuracy. Another important factor
that may cause substantial impact to GLCM processing is the
kernel size. Moreover, the method is not able to properly
identify the boundary region between the textures.
Markov random field (MRF) model (Szira yi et al. 2000; Clausi
et al. 2004; Chellappa et al. 1985; Solberg et al. 1996; Chen et
al. 1998) assumes that the intensity at each pixel in the image
depends on the intensities of the neighboring pixels. The
method segment the image based on the local calculations of
probability and potential functions. The draw back of the
method is that, it cannot properly handle the data from classes
not present in the training set, erodes the quality of the
segmentation in the boundary region and needs huge amount of
computing power.
Some more techniques like the gamma, Weibull, and K
distributions have been used to classify microwave images
(Bernad et al. 2009; Dong et al. 2001; Tison et al. 2004; Petrou
et al. 2002). These mathematical models have done well in
characterizing low-resolution microwave images. But most of
them failed to classify high-resolution microwave images due
to presence of complex structure of textures in such type of
images. RISAT II is capable of high-resolution microwave
imaging of Im, 3m, 1.8m and 8m. Therefore classification of
this type of high-resolution microwave images without much
human intervention is required to be developed.
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