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Proceedings Paper

Bayesian classification of multi look polarimetric SAR images with a generalized multiplicative speckle model
Author(s): Guoqing Liu; ShunJi Huang; Andrea Torre; Franco S. Rubertone
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Paper Abstract

In this paper, a maximum likelihood (ML) classification algorithm is proposed to classify multi-look polarimetric SAR images. This algorithm considers a generalized multiplicative speckle model in which three texture factors are assumed to separately affect three polarization channels. We derive the ML estimation of the texture parameters for each polarization channel with the complex Wishart distribution of the multi-look speckle covariance matrix, and design the corresponding ML classifier according to the Bayesian criterion.BOth the texture class statistics and the discriminant function are given in simple closed forms. Further, a method for adaptively producing the a priori probabilities is also presented in order to improve the classification accuracy. This method utilizes the contextual information in a forward procedure, and does not need any iteration. With the NASA/JPL L-band 4-look polarimetric SAR data, the effectiveness of the presented classification algorithm is demonstrated, and using of the adaptive a priori probabilities is shown to result in improved classifications.

Paper Details

Date Published: 28 July 1997
PDF: 8 pages
Proc. SPIE 3070, Algorithms for Synthetic Aperture Radar Imagery IV, (28 July 1997); doi: 10.1117/12.281578
Show Author Affiliations
Guoqing Liu, Univ. of Electronic Science and Technology (China)
ShunJi Huang, Univ. of Electronic Science and Technology (China)
Andrea Torre, Alenia Spazio SpA (Italy)
Franco S. Rubertone, Alenia Spazio SpA (Italy)


Published in SPIE Proceedings Vol. 3070:
Algorithms for Synthetic Aperture Radar Imagery IV
Edmund G. Zelnio, Editor(s)

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