Urban Building Change Detection Model with Automated Modeling of the Directional Relationships Between Buildings and Shadows

Due to the variability in bi-temporal high-resolution images, such as sensor, shooting angle and seasonal turnover, pseudo-change phenomena often exhibited in urban building change detection. To solve this problem, a new network model AMDR-Net is proposed, which integrates the modeling of the directional relationship between buildings and their shadows. The existing modeling methods for the directional relationship between buildings and shadows rely on the metadata of solar elevation. To meet this challenge, AMDR-Net constructs a network with a CNN architecture. Automatically learn the direction relationship between buildings and shadows from high-resolution remote sensing images. Experiments conducted on the Fuzhou dataset demonstrate that the proposed method achieves high consistency with ground-truth change maps, with an F1-score of 90.83. The results indicate that AMDR-Net exhibits superior robustness and performance in mitigating false changes caused by image disparities, outperforming existing comparative methods.