期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 42, 期 10, 页码 2063-2072出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2004.835302
关键词
hierarchical image segmentation; maximum-like-lihood estimation; polarimetric synthetic aperture radar (SAR); image; texture; Wishart and K-distributions
A hierarchical stepwise optimization process is developed for polarimetric synthetic aperture radar image segmentation. We show that image segmentation can be viewed as a likelihood approximation problem. The likelihood segment merging criteria are derived using the multivariate complex Gaussian, the Wishart distribution, and the K-distribution. In the presence of spatial texture, the Gaussian-Wishart segmentation is not appropriate. The K-distribution segmentation is more effective in textured forested areas. The validity of the product model is also assessed, and a field-adaptable segmentation strategy combining different criteria is examined.
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