Accurate Water Body Mapping Based on Unsupervised Deep Learning

Rapid and accurate monitoring of surface water is critical for water resource management, environmental protection, sustainable urban development, among other issues. Traditional threshold-based or classification-based surface water mapping methods often require adjusting thresholds or training samples for different regions or different sensors, which may hinder the generalization performance of the method in large-scale water body mapping. We propose an unsupervised deep learning water body mapping framework (UUCP) for unlabeled large-scale optical remote sensing images in this study. The UUCP framework adopts an unsupervised multi-segment thresholding strategy to achieve the transition from label-free learning to noisy label learning, and learns robust multi-scale features of water bodies by the developed channel attention multi-scale surface water extraction network and training strategies under noise labels. The results show that our proposed method performs well in the overall performance of water extraction and is applicable to different sensors.