期刊
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
卷 93, 期 -, 页码 123-135出版社
ELSEVIER
DOI: 10.1016/j.isprsjprs.2014.04.010
关键词
Change detection; Multivariate generalized Gaussian model; Robust principal component analysis; Graph cuts; Synthetic aperture radar
类别
资金
- National Natural Science Foundation of China [61273317]
- National Top Youth Talents Program of China
- Fundamental Research Fund for the Central Universities [K5051202053]
- Specialized Research Fund for the Doctoral Program of Higher Education [20130203110011]
In this paper, a novel change detection approach is proposed for multitemporal synthetic aperture radar (SAR) images. The approach is based on two difference images, which are constructed through intensity and texture information, respectively. In the extraction of the texture differences, robust principal component analysis technique is used to separate irrelevant and noisy elements from Gabor responses. Then graph cuts are improved by a novel energy function based on multivariate generalized Gaussian model for more accurately fitting. The effectiveness of the proposed method is proved by the experiment results obtained on several real SAR images data sets. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
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