Research Papers

Novel mixture model for synthetic aperture radar imagery

[+] Author Affiliations
Qiangqiang Peng

Beihang University, School of Automation Science and Electrical Engineering, Beijing 100191, China

Long Zhao

Beihang University, School of Automation Science and Electrical Engineering, Beijing 100191, China

J. Appl. Remote Sens. 6(1), 063616 (Dec 13, 2012). doi:10.1117/1.JRS.6.063616
History: Received July 6, 2012; Revised November 27, 2012; Accepted November 29, 2012
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Abstract.  We propose a novel mixture model that combines two special cases of heavy-tailed Rayleigh distribution. These two special families possess the only analytical forms of heavy-tailed Rayleigh distribution. As a consequence, the mixture model has an analytical form. Because heavy-tailed Rayleigh distribution is a member of spherically invariant random process, one can obtain the parameter estimation by the method-of-moments technique. Finally, the mixture model has been tested on various synthetic aperture radar images, and the performance of this model is strong compared with other models such as K distribution, G0 distribution, and heavy-tailed Rayleigh models.

© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Qiangqiang Peng and Long Zhao
"Novel mixture model for synthetic aperture radar imagery", J. Appl. Remote Sens. 6(1), 063616 (Dec 13, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063616


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