ViewCAM: A Weakly Supervised Building Extraction Method Based on View Consistency and Feature Affinity Enhancement

In building extraction, collecting pixel-level annotations required by fully supervised methods is extremely costly. Image-level weakly supervised methods based on class activation maps (CAMs) effectively reduce the cost and have shown promising progress. However, generating high-quality CAMs remains challenging due to the supervision gap between classification and segmentation tasks. Specifically, image-level supervision causes CAMs to activate only the most discriminative regions, which compromises the integrity of CAMs. Meanwhile, the absence of pixel-level supervision leads to a depletion of spatial information, resulting in imprecise boundaries. In this study, we propose a novel image-level weakly supervised building extraction method based on view consistency, named ViewCAM, to generate high-quality CAMs. The view transformation module is designed to apply view transformations to remote sensing images and the high-dimensional features. Additionally, a feature affinity enhancement module (FAEM) is proposed to capture positional relationships between pixels and low-level features, such as edges and textures, improving boundary fineness. We integrate these two modules into a classification network and incorporate pixel-level supervision using view-consistency constraints. The entire network is then trained in an end-to-end manner, leading to improved integrity and boundary fineness of the seeds generated from CAMs. To verify the effectiveness and robustness of ViewCAM, we conduct experiments on two representative datasets, and the results demonstrate that our proposed method achieves superior CAM integrity and boundary fineness, outperforming state-of-the-art methods.