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Supervised SAR Image MPM Segmentation Based on Region-Based Hierarchical Model

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3 Author(s)
Yong Yang ; Signal Process. Lab., Wuhan Univ. ; Hong Sun ; Chu He

This letter presents a novel method of supervised multiresolution segmentation for synthetic aperture radar images. The method uses a region-based half-tree hierarchical Markov random field model for multiresolution segmentation. To form the region-based multilayer model, the watershed algorithm is employed at each resolution level independently. The nodes of a quadtree in the proposed model are defined as regions instead of pixels. The relationship over scale is studied, and the region-based upward and downward maximization of posterior marginal estimations are deduced. The experimental results for the segmentation of homogeneous areas prove the region-based model much better in terms of robustness to speckle and preservation of edges compared to the pixel-based hierarchical model and the Gibbs sampler with the single-resolution model

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:3 ,  Issue: 4 )

Date of Publication:

Oct. 2006

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