Shape Activated CAM Learning for Weakly Supervised Remote Sensing Semantic Segmentation

Class activation map (CAM)-based weakly supervised semantic segmentation (WSSS) of remote sensing (RS) images has attracted extensive research interests for its potential in reducing annotation cost. However, challenged by unconstrained activation issue, existing methods struggle to delineate object boundaries clearly, making them particularly difficult to separate multiple densely packed objects, which are common in RS images. By conducting an in-depth analysis of RS image characteristics, we observed a strong correlation between object shapes and their semantics. Inspired by this finding, we propose an intrinsic shape activation network (ISANet) to learn the category-relevant shape priors as geometry constraints for target-focused region activation in WSSS of RS images. The key idea is to distill the intrinsic shape priors from the hybrid features that are deterministic in classification. Specifically, we adopt a dual-branch architecture to decouple the learning of shape and texture features and leverage a shape awareness alignment module (SAM) to generate boundary-clear CAMs for computing pseudo-labels. In this way, CAMs are generated with perception of target shapes, which increases the completeness of activation regions and alleviates the ultrarange responses. Extensive experiments demonstrate the superiority of our method in delineating densely packed objects with clear contours, which is especially beneficial for separating multiple targets in RS images. Our method improves the mean intersection over union (mIoU) of the state-of-the-art method by 7.9% and 3.3% on the NWPU VHR-10 and iSAID dataset, respectively.