Change Detection Based GGR-GKIT On SAR Amplitude Image
Conference: EUSAR 2012 - 9th European Conference on Synthetic Aperture Radar
04/23/2012 - 04/26/2012 at Nuremberg, Germany
Proceedings: EUSAR 2012
Pages: 4Language: englishTyp: PDF
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Authors:
Wang, Hu-Qing; Li, Heng-Chao (Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University Chengdu, 610031, China)
Huang, Ping-Ping (College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China)
Abstract:
In this paper, a novel approach to change detection from synthetic aperture radar (SAR) amplitude imagery is presented. In particular, a parametric pdf is proposed to model the ratio image, which is based on an efficient statistical model generalized Γ distribution (GGammaD). The GGammaD forms a large variety of alternative distributions and is flexible to model the SAR images covering different kinds of surfaces in amplitude and intensity formats. In addition, a SAR specific parametric modeling approach and a suitable parameter-estimation based on the method of log-cumulants (MoLC) of the generalized Γ ratio (GGammaR) distribution are adopted. Specifically, due to the considerable non-Gaussianity of SAR amplitude ratio data, the aforementioned parametric modeling approach for the ratio image is integrated into the generalized Kittler and Illingworth minimum-error thresholding (GKIT) process which allows one to take into account non-Gaussian data. Experimental results obtained confirm the effectiveness of the proposed approach.