Change detection methods based on low-rank sparse representation for multi-temporal remote sensing imagery

With the development of remote sensing image applications, remote sensing imagery is an important technology to make a dynamic detection for changes of lands or coastal zones. Though high resolution remote sensing imagery provides very good performance and large information for the spatial structure variation, the limitation of its spatial structure makes change detection difficult. In this paper, we propose two change detection methods for multi-temporal remote sensing images which are based on low-rank sparse decomposition and based on low-rank matrix representation. An observation matrix is constructed by ordering each band of remote sensing images into a vector. We utilize bilateral random projection method to make low rank decomposition to get a sparse matrix. We then obtain the change map by using nearest neighbor to cluster the change parts from the sparse matrix. On the other hand, by dividing the difference set of multi-temporal remote sensing images into non-overlapping squares with equal size and tiling these squares, an observation matrix is built up. We make up a feature space matrix by low rank matrix representation to build a sparse representation model, and combine nearest neighbor method to make change detection for multi-temporal remote sensing dataset. This change detection method is addressed by iterating between kernel norm minimization and sparsity minimization. The experimental results show that our proposed methods perform better in detecting changes than the other change detection methods.