4 December 1998 Multiresolution wavelet analysis for SAR image segmentation using statistical separability measures
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chi Hau Chen, Chi Hau Chen, Yang Du, Yang Du, } "Multiresolution wavelet analysis for SAR image segmentation using statistical separability measures", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); doi: 10.1117/12.331854; https://doi.org/10.1117/12.331854
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