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Complementary information contained in each of the individual polarization mode of fully polarimetric PALSAR data is fused using Expectation Maximization (EM) algorithm to improve information contents in the fused image. A comparative analysis of supervised classifiers viz. parallelepiped and minimum distance is accomplished to assess the suitability of the particular classifier. It is also demonstrated...
In this paper, an attempt has been made to improve information of an image by fusion technique, so it may be more effectively used. Full polarimetric images of ALOS PALSAR are fused by exploiting the nice properties of Expectation Maximization (EM) algorithm. Maximum likelihood classifier is applied on the composite unfused singlet and fused doublet images and results are compared on the basis of...
The structure of the specialized processor of signal processing in piecewise-polynomial bases was developed and tested. By using MATLAB environment with application standard Simulink we were able to model the algorithms and structure of the specialized processor.
The present paper deals with the potential application of fully polarimetric data in identification of various land cover types based on polarimetric indices, namely, backscattering coefficients and their ratios of various polarizations (linear, circular, linear 45°), entropy, weighted polarimetric sum, correlation coefficient, normalized difference polarization index, and ratio vegetation index....
Polarimetric SAR provides a substantial source of information over conventional imaging radars. Fully polarimetric PALSAR data has been used for land cover classification. Co and Cross Polarization signatures for different classes are obtained in linear (h,v) and circular (l.r) basis. Different combinations from both basis have been extracted for the (principal component analysis) PCA. Supervised...
In present paper an attempt has been made for unsupervised classification of SAR images based on the surface roughness using multifractal technique. Surface roughness is measured with the help of fractal dimension (D), which lies in the range 2.0 and 3.0. Based on roughness values, i.e., D, various land classes are grouped in different classes. The D values are estimated for a number of local window...
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