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 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 pr oposed algorithm has a good change detection results and also performs quite well on denoising purpose.