Progressive Difference Amplification Network With Edge Sensitivity for Remote Sensing Image Change Detection

Capturing finer and discriminative difference features (DFs) is key to obtaining a high-quality change detection (CD) map. However, there is still significant scope for further study on fine-grained detection, especially concerning terms of improving structural integrity and reducing internal holes or sticking in DF. To this end, we propose a progressive difference amplification network (PDANet) with edge sensitivity to detect changed areas in optical remote sensing images (RSIs), where the key point is to amplify DF and reinforce edge detail to improve CD accuracy. The edge sensitivity (ES) encoder is designed to capture the long-distance dependency, which compensates for the limited receptive fields of the convolutional neural network with fixed kernels. Meanwhile, we introduce the prior edge in the network training stage, which collaborates with the ESE to improve the structural integrity of the changed areas. On the other hand, the difference amplification decoder is proposed to enhance the representation of the changed areas, and it is achieved by integrating multiscale DF and reconstructing the original single RSI using DF as full-stage guidance. Finally, the CD map and edge map are predicted based on the reconstructed feature and the maximum scale DF. Extensive experiments on one instance dataset and three CD benchmark datasets demonstrate that PDANet outperforms the state-of-the-art CD competitors both qualitatively and quantitatively.