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Simulating high-resolution Synthetic Aperture Radar (SAR) images in complexscenes has consistently presented a significant research challenge. Thedevelopment of a microwave-domain surface scattering model and itsreversibility are poised to play a pivotal role in enhancing the authenticityof SAR image simulations and facilitating the reconstruction of targetparameters. Drawing inspiration from the field of computer graphics, this paperproposes a surface microwave rendering model that comprehensively considersboth Specular and Diffuse contributions. The model is analytically representedby the coherent spatially varying bidirectional scattering distributionfunction (CSVBSDF) based on the Kirchhoff approximation (KA) and theperturbation method (SPM). And SAR imaging is achieved through the synergisticcombination of ray tracing and fast mapping projection techniques. Furthermore,a differentiable ray tracing (DRT) engine based on SAR images was constructedfor CSVBSDF surface scattering parameter learning. Within this SAR imagesimulation engine, the use of differentiable reverse ray tracing enables therapid estimation of parameter gradients from SAR images. The effectiveness ofthis approach has been validated through simulations and comparisons with realSAR images. By learning the surface scattering parameters, substantialenhancements in SAR image simulation performance under various observationconditions have been demonstrated.

Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing