Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery
This study addresses the challenges of missing annotations and ambiguous boundaries in polar low segmentation from SAR imagery by proposing CREST, a weakly supervised framework. The method incorporates a CORE module to encode spatial connectivity priors, combined with constrained region expansion and dynamic self-bootstrapping loss to effectively suppress background noise and refine pseudo-label quality. Experiments on Sentinel-1 data demonstrate that CREST accurately reconstructs cyclone structures and generates multi-level reliability masks. Furthermore, it outperforms state-of-the-art adversarial erasure methods on ultrasound and VOC datasets. These results establish CREST as an efficient solution for complex object segmentation in scenarios lacking pixel-level annotations, offering robust performance across diverse imaging domains through its novel integration of topological priors and adaptive learning mechanisms.