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Proceedings Paper

Statistical analysis of multilook polarimetric SAR data and terrain classification with adaptive distribution
Author(s): Guoqing Liu; ShunJi Huang; Andrea Torre; Franco S. Rubertone
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Paper Abstract

This paper deals with analysis of statistical properties of multi-look processed polarimetric SAR data. Based on an assumption that the multi-look polarimetric measurement is a product between a Gamma-distributed texture variable and a Wishart-distributed polarimetric speckle variable, it is shown that the multi-look polarimetric measurement from a nonhomogeneous region obeys a generalized K-distribution. In order to validate this statistical model, two of its varied versions, multi-look intensity and amplitude K-distributions are particularly compared with histograms of the observed multi-look SAR data of three terrain types, ocean, forest-like and city regions, and with four empirical distribution models, Gaussian, log-normal, gamma and Weibull models. A qualitative relation between the degree of nonhomogeneity of a textured scene and the well-fitting statistical model is then empirically established. Finally, a classifier with adaptive distributions guided by the order parameter of the texture distribution estimated with local statistics is introduced to perform terrain classification, experimental results with both multi-look fully polarimetric data and multi-look single-channel intensity/amplitude data indicate its effectiveness.

Paper Details

Date Published: 21 November 1995
PDF: 12 pages
Proc. SPIE 2584, Synthetic Aperture Radar and Passive Microwave Sensing, (21 November 1995); doi: 10.1117/12.227130
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. 2584:
Synthetic Aperture Radar and Passive Microwave Sensing
Giorgio Franceschetti; Christopher John Oliver; James C. Shiue; Shahram Tajbakhsh, Editor(s)

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