A Weakly Supervised Semantic Segmentation Approach for Damaged Building Extraction From Postearthquake High-Resolution Remote-Sensing Images.

Quick and accurate building damage assessment following a disaster is critical to making a preliminary estimate of losses. Remote-sensing image analysis based on convolutional neural networks (CNNs) and their relatives has shown a growing potential in this task, but faces the challenge of collecting dense pixel-level annotations. In this letter, we propose a novel weakly supervised semantic segmentation (WSSS) method based on image-level labels for pixel-wise damaged building extraction from postearthquake high-resolution remote-sensing (HRRS) images. The proposed method aims to improve the quality of the class activation map (CAM) to boost model performance. To be specific, a multiscale dependence (MSD) module and a spatial correlation refinement (SCR) module are designed by considering the special characteristics of the damaged building and are integrated into an encoder-decoder network. The former is used for complete and dense localization of damaged buildings in CAM, and the latter contributes to noise suppression. Extensive experimental evaluations over three datasets are conducted to confirm the effectiveness of the proposed approach. Both generated CAMs and extracted damaged building results of our methods are better than that of current state-of-the-art methods.