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

被引：32

作者：

Su, Xin ^{[1
]}

He, Chu ^{[1
]}

Feng, Qian ^{[1
]}

Deng, Xinping ^{[1
]}

Sun, Hong ^{[1
]}

机构：

[1] Wuhan Univ, Sch Elect Informat, Wuhan 430072, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2011年 / 8卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Conditional random fields (CRF); ESAR Image; image classification; iteration reasoning; multiscale region connection calculus;

D O I：

10.1109/LGRS.2010.2089427

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

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.

引用

页码：497 / 501

页数：5

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