Physics-Aware Deep Learning for SAR and InSAR Remote Sensing: Models, Methods, and Open Challenges

SAR and InSAR are fundamental sensing modalities for all-weather, day-and-night Earth observation because they operate independently of solar illumination and retain sensitivity to scene structure under conditions that often limit optical imaging. Recent Deep Learning (DL) methods have improved SAR image interpretation, inverse imaging, target recognition, and InSAR-based deformation analysis, but many purely data-driven pipelines still neglect the forward sensing model, coherent scattering physics, speckle statistics, phase structure, and acquisition geometry that shape radar observations. This paper develops a physics-aware perspective on learning for SAR and InSAR. The literature is organized along two complementary axes: the physical constraint dimensions that govern radar measurements, namely polarization, scattering, signal-domain structure, resolution, and interferometric phase/coherence, and the integration modes through which such structure enters modern learning systems, including representation design, model-guided architectures, and learning-assisted inverse problems. We further discuss representative applications, dataset and evaluation issues, domain-shift challenges, and trustworthy deployment. Finally, two constructive illustrative case studies are included: one on sparse SAR imaging with an explicit SAR sensing matrix, and one on InSAR phase-domain estimation under wrapped phase, coherence variation, and physics-aware regularization. Together, they illustrate how physics-aware learning compares with classical structure-preserving baselines and unconstrained black-box learning in representative amplitude-driven and phase-driven inverse settings.