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A Supervised Classification Method Based on Conditional Random Fields With Multiscale Region Connection Calculus Model for SAR Image

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5 Author(s)
Xin Su ; Sch. of Electron. Inf., Wuhan Univ., Wuhan, China ; Chu He ; Qian Feng ; Xinping Deng
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This letter presents a supervised classification method for synthetic aperture radar (SAR) images based on multiscale region connection calculus (RCC) and conditional random fields (CRF). Using this method, first, a SAR image is oversegmented into multisuperpixels via the image pyramid. We then use the multiscale RCC model to describe the spatial logic relationships among these superpixels. To complete the process, multiscale RCC relationships are learned and reasoned under the CRF reasoning framework. This method employs iteration strategy for CRF reasoning to get better details in the classification results as well. We illustrate the proposed method by experiments conducted on DLR ESAR image. The results reveal efficient performance.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:8 ,  Issue: 3 )