DCSRL: a change detection method for remote sensing images based on deep coupled sparse representation learning

Change detection methods include model-driven methods and data-driven methods. Model-driven methods are rigorous in theory but have difficulties in establishing accurate models for complex problems. Data-driven methods have strong learning abilities, but it is difficult to interpret their mechanism. In order to comprehensively utilize their advantages to highlight changes in multi-temporal remote sensing images. We proposed a change detection method for remote sensing images based on deep coupled sparse representation learning, in which the coupled sparse representation learning is implicitly expressed by convolutional neural networks (CNNs). A coupled sparse representation learning method is first proposed, and then it is unfolded into a CNNs network, the coupled sparse coefficient and dictionary are learned from the training data. Unlike fully data-driven CNNs, auxiliary coupled sparse representation is utilized to guide CNNs to identify the changed areas. Finally, the experiments on two datasets verified the validity of the proposed method.