Flood extent mapping in SAR images using semi-supervised approach - Girisha S; Savitha G; Sughosh P (Results in Engineering)
Floods pose a significant threat to both human populations and critical infrastructure. They are caused by excessive precipitation, snowmelt, or infrastructure failures. Precise mapping of flood levels is essential for directing emergency response, allocating resources as efficiently as possible, and estimating the degree of damage. Accurate semantic segmentation of flood-zones from SAR images is necessary for disaster management as it reduces human error and facilitates fast decision-making. However, the complexity of SAR images and the scarcity of annotated datasets make it difficult to develop efficient computer vision algorithms for this use. The paper employs consistency regularization and pseudo-label generation to overcome these issues and presents a novel hybrid semi-supervised semantic segmentation method. In particular, the study presents a student-teacher paradigm in which robust feature learning is ensured by training the teacher model through consistency regularization and self-supervised learning. An attention module-based tailored student model is developed to precisely identify flood-zones. Auxiliary decoders are also added throughout the training phase to reinforce consistency regularization and help the model capture extensive hierarchical characteristics. The suggested method was assessed with publicly accessible datasets, and the outcomes show notable gains in both qualitative and quantitative measures. The findings highlight the method's effectiveness in improving flood-zone segmentation accuracy, leading to more efficient disaster management.