Multitemporal image change detection with compressed sparse representation

In this paper, we propose a novel feature vector clustering method for unsupervised change detection in multitemporal satellite images. A feature vector for each pixel is extracted using the compressed sparse representation of the difference image which is obtained by comparing a pair of co-registered images acquired at different times on the same area. The compressed sparse representation is achieved by taking two stages: compressed sampling and sparse representation. The compressed sampling is first employed in order to reduce the dimensionality of the feature vectors. Then, the sparse representation is applied to extract the meaningful change information and to combat the noise interference. The final change detection is obtained by clustering the extracted feature vectors using k-means algorithm into “changed” and “unchanged” classes. Experimental results clearly show that the proposed approach consistently yields superior performance compared to several well-known change detection techniques on both noise-free and noisy satellite images.