Novel mixture model for synthetic aperture radar imagery

Qiangqiang Peng; Long Zhao

doi:10.1117/1.JRS.6.063616

13 December 2012 Novel mixture model for synthetic aperture radar imagery

Qiangqiang Peng, Long Zhao

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 6, Issue 1, 063616 (December 2012). https://doi.org/10.1117/1.JRS.6.063616

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.

Citation Download Citation

Qiangqiang Peng and Long Zhao "Novel mixture model for synthetic aperture radar imagery," Journal of Applied Remote Sensing 6(1), 063616 (13 December 2012). https://doi.org/10.1117/1.JRS.6.063616

Published: 13 December 2012

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