Water Region Segmentation in SAR Images Based on Compact Operational Unets

In this work, we propose a novel supervised segmentation approach based on compact Operational U-Nets (Op-UNets) for detection of water regions in high-resolution synthetic aperture radar (SAR) images. The proposed approach utilizes the Segment Anything Model (SAM) for supervised training of separate compact Op-UNet model to automatically generate higher accuracy binary segmentation masks from the ground truth optical imagery. To the best of our knowledge, this is the first study that applies jointly the SAM model and compact Op-UNet architecture which utilizes the self-organized operational neural network (self-ONN) layers to improve SAR image segmentation performance with limited labeled data. The preliminary results from the performed experiments using the high-resolution SAR imagery over the coastline along Daytona Beach and Seminole, FL (from Capella Space) are presented to evaluate the performance of the proposed model with significantly reduced computational complexity.