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Proceedings Paper

Statistical and separability properties of the polarimetry SAR matrix elements
Author(s): Ala Bahrami; Mahmoud R. Sahebi; Mohammad J. Valadan Zouj; Salman Ahmadi; Maryam Dehghani; Anand Nandipati
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Paper Abstract

The development of the polarimetric synthetic aperture radar (PolSAR) applications has been accelerated by coming of new generation of SAR polarimetric satellites (TerraSAR-X, COSMO-SkyMed, RADARSAT-2, ALOS, etc.). The aim of this article is to extract the information content of the polarimetric SAR data. Cross products of four channels "HH, HV, VH, and VV" could be at least nine features in vector space and by applying the different class separability criterion, the impacts of each feature, for extracting different patterns, could be tested. We have chosen the large distance between classes and small distance within-class variances as our criterion to rank the features. Due to high mutual correlation between some of the features, it is preferable to combine the features which result in the lower number of features. Also the computational complexity will be decreased when we have lower number of features. Due to these advantages, our goal would be to decrease the number of features in vector space. To achieve that, a subset of ranked features consists of two to nine ranked features will be classified and the classification accuracy of different subsets will be evaluated. It is possible that some of the new features that have been added to the old subsets change the classification accuracy. Finally different feature subsets which were selected based on the various class-separability approaches will be compared. The subset that gives the highest overall accuracy would be the best representative of the nine originally features.

Paper Details

Date Published: 29 September 2009
PDF: 8 pages
Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74771Z (29 September 2009); doi: 10.1117/12.830177
Show Author Affiliations
Ala Bahrami, K.N. Toosi Univ. of Technology (Iran, Islamic Republic of)
Mahmoud R. Sahebi, K.N. Toosi Univ. of Technology (Iran, Islamic Republic of)
Mohammad J. Valadan Zouj, K.N. Toosi Univ. of Technology (Iran, Islamic Republic of)
Salman Ahmadi, K.N. Toosi Univ. of Technology (Iran, Islamic Republic of)
Maryam Dehghani, K.N. Toosi Univ. of Technology (Iran, Islamic Republic of)
Anand Nandipati, Westfälische Wilhelms-Univ. (Germany)


Published in SPIE Proceedings Vol. 7477:
Image and Signal Processing for Remote Sensing XV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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