MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change Detection

Remote sensing (RS) image change detection (CD) aims to identify and localize the differences in the same geographical area observed across bi-temporal RS images. Existing CD methods typically focus on characterizing and distinguishing change regions, while often overlooking the non-change areas (background information) in RS images. Therefore, the influence of subtle changes in non-change regions may be amplified, leading to false negatives or false positives in the change regions. To address this issue, we propose a multi-view complementary learning network (MVCL) for RS image CD, which considers change detection from both background view and change view perspectives, while focusing on the extraction and aggregation of both change and non-change (background) features. Specifically, we introduce a background branch during the encoder stage dedicated to extracting background information. This background information is then injected into the change regions through the background-dependent feature fusion module (BFFM), serving as prior knowledge. By leveraging the stable background information, irrelevant and noisy changes are suppressed, guiding the generation of change regions and reducing the interference of background noise. Furthermore, to mitigate the noise introduced during multi-scale fusion in the non-change (background) areas, we propose a prior-guided spatial background enhancement module (PGBM), which consists of a prior semantic guidance strategy and a spatial background enhancement module. According to the results of experiments conducted on three public datasets, the proposed method outperforms several SOTA methods in both quantitative metrics and visual effectiveness. The source code is available at https://github.com/AdKingH/MVCL.