Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing

The simulation of high-resolution Synthetic Aperture Radar (SAR) imagery in intricate environments remains a formidable challenge. Advancements in reversible microwave-domain surface scattering models are crucial, potentially revolutionizing the fidelity of SAR simulations and streamlining the extraction of target parameters. Drawing inspiration from computer graphics, this paper proposes a novel differentiable ray tracing (DRT) approach for microwave rendering and fast SAR imaging. The rendering model utilizes coherent spatially varying bidirectional scattering distribution function (CSVBSDF) based on the Kirchhoff approximation (KA) and the small perturbation method (SPM), corresponding to specular and diffuse scattering contributions, respectively. SAR imaging is efficiently executed via a fusion of ray tracing and rapid mapping projection. The innovative DRT reversible engine enables swift estimation of SAR image parameter gradients for direct CSVBSDF surface scattering parameter optimization. The method’s validity is confirmed through comparative analysis with measured SAR images and other method, demonstrating marked improvements in SAR simulation fidelity across diverse observational scenarios by learning surface scattering parameters.