SAR Image Segmentation

SAR image segmentation aims to automatically partition SAR images into meaningful regions, facilitating analysis of diverse features like sea ice, urban areas, or land cover. Current research emphasizes developing robust algorithms, including active contour models and deep learning architectures like ResNets and U-Nets, that address challenges posed by noise (e.g., speckle) and the inherent ambiguity of SAR data. These advancements improve accuracy and efficiency in applications ranging from autonomous navigation to environmental monitoring, particularly benefiting from techniques like weakly supervised learning and the integration of data from other sensor modalities (e.g., LiDAR, EO).