PDE-Based Physics Guided Neural Network for SAR Image Segmentation

Academicians and researchers have been keen on climatic and maritime monitoring. Synthetic Aperture Radars (SAR) have been instrumental in capturing images of the ocean. Sea-ice classification using Sentinel-1, a type of SAR, has gained popularity over the years. Automatic image segmentation is a fundamental task in SAR image analysis. This paper proposes a novel, innovative approach to SAR image segmentation by integrating physics-based knowledge into a neural network framework. The implementation of a PDE for SAR image segmentation offers a promising avenue for advancing the field of remote sensing. PDEs provide the required physics for the model. This integration not only enhances the network’s capability to learn meaningful features but also enables it to generate more interpretable and physically meaningful segmentation maps. The Partial Differential Equations (PDE) based Physics-Guided Neural Network (PGNN) model can achieve an accuracy of about 96%, which is greater than all existing state-of-art techniques. By harnessing the synergy between deep learning and physics-based knowledge, this work not only improves segmentation accuracy but also facilitates a deeper understanding of SAR data, paving the way for more reliable and insightful remote sensing applications.