Physics-Informed Deep Learning for SAR image focusing in view of onboard SAR applications

Abstract

Onboard SAR processing is a key enabler for low-latency, autonomous Earth Observation (EO) missions. However, conventional SAR focusing methods are computationally intensive and hard to be optimized for real-time execution on resource-constrained platforms, limiting the feasibility of high-level application onboard, such as object detection, situational awareness, and real-time monitoring capabilities. This contribution proposes a physics-informed deep learning (DL)-based approach for the onboard focusing of SAR amplitude images. The method approximates the output of conventional focusing algorithms using a convolutional neural network (CNN), specifically designed by considering the theoretical principles underlying SAR image formation, and provides focused amplitude SAR images.