AANet: An Ambiguity-Aware Network for Remote-Sensing Image Change Detection

Remote-sensing image change detection (CD) task plays an important role in land-use surveys, city construction investigations, and other vital industries. Recently, deep learning has become a mainstream method for this task due to its satisfactory performance in most cases. However, it often suffers from difficulties in dealing with ambiguous regions, where pseudo-changes happen or real changes are corrupted. In this article, we propose an ambiguity-aware network (AANet) to address the aforementioned issue. Specifically, our network first adopts convolutional layers to learn features from dual-temporal images. After that, an ambiguity refinement module (ARM) is designed to extract the ambiguity regions and then difference features are generated based on it. Considering that the scales of different changed objects vary, a weight rearrangement module (WRM) is proposed to fuse the difference features from different layers. To test the performance of our proposed model, we conduct experiments on three benchmark datasets, including SYSU-CD, SVCD, and LEVIR-CD. The experimental results show that our model can outperform several state-of-the-art models on all three datasets, which validates its effectiveness. The source code of our proposed model will be released at https://github.com/KevinDaldry/AANet.