BSG-WSL: BackScatter-guided weakly supervised learning for water mapping in SAR images
Year: 2025
Type: article
Abstract: Extracting and analyzing water resources in Synthetic Aperture Radar (SAR) images is crucial for flood management and environmental resource planning due to the ability to monitor ground all-weather and all-time. However, extracting water entirely from high-resolution SAR images in diverse scenarios is challenging due to variable water shapes, many low-intensity land covers similar to water, and scarce labels. In this article, a BackScatter-Guided Weakly Supervised Learning (BSG-WSL) framework based on image-level labels is proposed for water extraction with the requirement of high generalization and low labeling. In BSG-WSL, a BackScatter-Guided Network (BSGNet) is proposed, where the backscatter information of water is used to guide the feature extraction process, yielding precise Class Attention Maps (CAMs) of water. Then, a morphological pseudo-label optimization algorithm is designed to employ CAMs to generate high-quality pseudo-labels. Finally, a confidence cross-entropy loss is introduced to utilize pseudo-labels to train the extraction model and achieve precise water extraction in different scenarios. Experiments on three datasets of SAR images from the GF-3 and Sentinel-1B satellites verify that the proposed method achieves state-of-the-art performance compared to other weakly supervised methods based on image-level annotations. • Lead the way with the application of WSSS for water extraction using image-level annotations. • Overcome the problem of high labor costs and false positives in water mapping. • Propose a backscatter-guided WSSS framework to improve the precision.
Authors: Kai Wang, Zhongle Ren, Biao Hou, Weibin Li, Licheng Jiao
Institution: Xidian University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,550
OpenAlex ID: W4407073152