SAR change detection based on intensity and texture changes

被引：73

作者：

Gong, Maoguo ^{[1
]}

Li, Yu ^{[1
]}

Jiao, Licheng ^{[1
]}

Jia, Meng ^{[1
]}

Su, Linzhi ^{[1
]}

机构：

[1] Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China

来源：

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING | 2014年 / 93卷

基金：

中国国家自然科学基金;

关键词：

Change detection; Multivariate generalized Gaussian model; Robust principal component analysis; Graph cuts; Synthetic aperture radar; UNSUPERVISED CHANGE DETECTION; OIL-SPILL SEGMENTATION; ENERGY MINIMIZATION; IMAGE; RETRIEVAL; FEATURES;

D O I：

10.1016/j.isprsjprs.2014.04.010

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

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.

引用

页码：123 / 135

页数：13

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