AMDANet: Augmented Multiscale Difference Aggregation Network for Image Change Detection

The field of remote sensing image change detection (CD) has made significant improvements with the rapid development of deep learning techniques. However, current methods often inadequately utilize difference features of bitemporal images, resulting in biased focus and insensitivity to change information. Furthermore, the classic challenges of pseudo-CD and edge recognition in complex scenes have also weakened CD performance. In this article, we propose an augmented multiscale difference aggregation network (AMDANet) for image CD, which incorporates a difference feature extractor (DFE) within a Siamese feature extractor to perceive changes by capturing differences between bitemporal features. To address the issue of biased focus, we propose a hierarchical feature aggregator (HFA) that captures intrascale interactions in parallel for multigranularity change perception while personalizing coarse-grained and fine-grained features to highlight the attention to change regions. To deepen the perception of complex dependency relationships, we further design an O-shape feature augmentor (OFA) that leverages an information feedback loop to achieve precise alignment of multigranularity features. The integration of information across different granularities improves the recognition of pseudo-changes and edges. Experimental results on three publicly available datasets demonstrate the superiority of AMDANet over current state-of-the-art (SOTA) methods. Our source code will be publicly available at https://github.com/mp-st/AMDANet.