USING COVARIANCE INTERSECTION FOR CHANGE DETECTION IN REMOTE SENSING IMAGES  

作　　者：Yang Meng Zhang Gong 

机构地区：[1]Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

出　　处：《Journal of Electronics(China)》2011年第1期87-94,共8页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.61071163)

摘　　要：In this paper,an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection(CI) to perform unsupervised fusion of the final fuzzy partition matrices from the Fuzzy C-Means(FCM) clustering for the feature space by applying compressed sampling to the given remote sensing images.The proposed approach exploits a CI-based data fusion of the membership function matrices,which are obtained by taking the Fuzzy C-Means(FCM) clustering of the frequency-domain feature vectors and spatial-domain feature vectors,aimed at enhancing the unsupervised change detection performance.Compressed sampling is performed to realize the image local feature sampling,which is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery.The experimental results demonstrate that the proposed algorithm has a good change detection results and also performs quite well on denoising purpose.In this paper, an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection （CI） to perform unsupervised fusion of the final fuzzy partition matrices from the Fuzzy C-Means （FCM） clustering for the feature space by applying compressed sampling to the given remote sensing images. The proposed approach exploits a CI-based data fusion of the membership function matrices, which are obtained by taking the Fuzzy C-Means （FCM） clustering of the fre- quency-domain feature vectors and spatial-domain feature vectors, aimed at enhancing the unsupervised change detection performance. Compressed sampling is performed to realize the image local feature sampling, which is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery. The experimental results demonstrate that the proposed algorithm has a good change detection results and also performs quite well on denoising purpose.

关 键 词：image Change detection Covariance Intersection （CI） FUSION SAR image MULTI-SPECTRAL 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]