Incorporating Directional Relationship Modelling for Building Change Detection from Very-High-Resolution Satellite Images

Detecting building changes from bitemporal very-high-resolution satellite images is often hindered by false positives, especially for tall structures, due to variations in sun azimuth and satellite viewing angles. To address this issue, we propose MDR-Net, a novel change detection network that models the directional relationship between buildings and their shadows. Unlike existing methods that rely on handcrafted rules and metadata (e.g., solar angles), MDR-Net automatically learns directional features from images using a customized directional modeling module (DMM). To further enhance feature representation, we introduce an adaptive feature fusion module (AFFM), which uses adaptive selection and dynamic adjustment strategies to emphasize critical features and effectively fuse directional information with bitemporal semantics. We evaluated MDR-Net on three building change detection data sets, i.e., Fuzhou (FZ), Wuhan (WH), and Guangzhou (GZ), and compared it with seven state-of-the-art methods: SMCD-Net-m, ChangeFormer, BIT, FC-Siam-diff, SNUNet, DGMA2-Net, and MSPSNet. The results showed that MDR-Net achieved the highest F1 scores of 93.84% and 82.22% on the FZ and WH data sets, respectively, evidently reducing false detections, particularly under large viewing-angle differences. In addition, our method, with fixed DMM weights and without sun azimuth information, successfully learns the directional relationship when retrained from the FZ to GZ data sets, demonstrating strong generalization to the public data set.