Traditional remote sensing image change detection approaches based on structure features are usually limited by imaging stability. In this paper

we introduce a new method for unsupervised change detection in remote sensing images using compressive sensing (CS) based on the image inherent sparse structures. For this algorithm

a large collection of image patches is projected onto high dimensional spaces through redundant dictionary

giving an adaptive sparse representation per each image patch. A random matrix is taken as measurement matrix to realize feature space dimension reduction. Then

the final change detection is realized by clustering the feature vector space using the fuzzy C-mean clustering(FCM)algorithm

achieving the reconstruction of change regional information. The experimental results demonstrate that the proposed algorithm has good change detection results both in contour and region and has a good robustness.