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Proceedings Paper

Multiresolution wavelet analysis for SAR image segmentation using statistical separability measures
Author(s): Chi Hau Chen; Yang Du
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Paper Abstract

A wavelet-based algorithm for polarimetric SAR imagery segmentation is developed. It utilizes the property that under wavelet transform correlation is very weak among the intensity HH, HV, VV channels as well as among the subimages in the same channel to form the effective feature vector for parametric segmentation. The statistical separability measures including the Bhattacharyya distance and a separability inhomogeneity function (SIF) are employed to extract the most effective feature vector in the sense of minimizing SIF. The algorithm is applied to the supervised segmentation of the polarimetric SAR imagery of San Francisco Bay area. It shows a good segmentation performance and a significant computational reduction. The segmentation result is also favorably compared with that of Gaussian modeling or mixture Gaussian modeling under non-feature extraction representation.

Paper Details

Date Published: 4 December 1998
PDF: 7 pages
Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); doi: 10.1117/12.331854
Show Author Affiliations
Chi Hau Chen, Univ. of Massachusetts/Dartmouth (United States)
Yang Du, Univ. of Michigan (China)


Published in SPIE Proceedings Vol. 3500:
Image and Signal Processing for Remote Sensing IV
Sebastiano Bruno Serpico, Editor(s)

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