A Cross-Angle Propagation Network for Built-Up Area Extraction by Fusing Spatial–Spectral-Angular Features From the ZY-3 Multiview Satellite Imagery: Dataset and Analysis of China’s 41 Major Cities

Obtaining timely and reliable built-up area (BUA) information across extensive geographical zones holds crucial significance for understanding environmental change and human activities. BUAs often exhibit detailed textures and structures in high-resolution imagery but also present strong heterogeneity. Current methods for BUA extraction primarily relied on planar information from single-view imagery, struggling to effectively capture the 3-D attributes of urban landscapes. Therefore, to address this challenge, this article proposes a cross-angle propagation network (CAPNet) based on multiview remote sensing stereo observation imagery. Our contributions are threefold: 1) we propose the cross-angle fusion module (CAFM) to exploit BUA’s complementary spatial-spectral-angular context across different viewing angles. This module leverages attention mechanisms for the automated acquisition of multiangle feature representation learning from diverse angle combinations. 2) We propose a multiangular propagation decoder (MAPD) that pioneers the exploration of gradually propagating multiangle disparity information through bidirectional-adjacent feature fusion across hierarchical levels. 3) We construct a large-scale, high-resolution multiview BUA (MVBA) dataset over China’s 41 major cities based on the ZY-3 satellites. Extensive experiment results on MVBA and the public WV-3 multiview semantic stereo datasets verify CAPNet’s superiority to existing state-of-the-art (SOTA) models, on preserving overall BUA shape, edge, and internal structures. The dataset and the source code of CAPNet will be publicly available at https://github.com/zuo-ux/Cross-Angle-Propagation-Network.