Cross-Orbital SAR Change Detection With A Physics-Informed Machine Learning Approach

This paper introduces a change detection method for Synthetic Aperture Radar (SAR) images acquired from different satellite orbits. Conventional methods struggle with angle sensitivity in SAR images, suffering from poor pixel correspondence between images. We propose an end-to-end framework that incorporates a semi-learnable feature transformation module. This module employs a re-sampling matrix, grounded in SAR imaging principles, to effectively align features extracted from images captured at disparate angles. Our approach is validated through three distinct scenarios: change detection in car parking lots across various orbits, also with orbital angles that are unseen during training, and flood detection where the training and testing using different satellite sources. The experimental results confirm that our method either achieve a competitive level equivalent to the other established methods, or effectively surpasses them in diverse scenarios.