Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing

TL;DR

This paper introduces a differentiable ray tracing approach to learn surface scattering parameters from SAR images, improving simulation accuracy and enabling parameter reconstruction for complex scenes.

Contribution

It presents a novel differentiable ray tracing engine for SAR image simulation that incorporates a comprehensive surface scattering model based on CSVBSDF, inspired by computer graphics.

Findings

Enhanced SAR image simulation accuracy under various conditions

Effective learning of surface scattering parameters from SAR images

Validation through simulations and real SAR image comparisons

Abstract

Simulating high-resolution Synthetic Aperture Radar (SAR) images in complex scenes has consistently presented a significant research challenge. The development of a microwave-domain surface scattering model and its reversibility are poised to play a pivotal role in enhancing the authenticity of SAR image simulations and facilitating the reconstruction of target parameters. Drawing inspiration from the field of computer graphics, this paper proposes a surface microwave rendering model that comprehensively considers both Specular and Diffuse contributions. The model is analytically represented by the coherent spatially varying bidirectional scattering distribution function (CSVBSDF) based on the Kirchhoff approximation (KA) and the perturbation method (SPM). And SAR imaging is achieved through the synergistic combination of ray tracing and fast mapping projection techniques. Furthermore, a differentiable ray tracing (DRT) engine based on SAR images was constructed for CSVBSDF surface scattering parameter learning. Within this SAR image simulation engine, the use of differentiable reverse ray tracing enables the rapid estimation of parameter gradients from SAR images. The effectiveness of this approach has been validated through simulations and comparisons with real SAR images. By learning the surface scattering parameters, substantial enhancements in SAR image simulation performance under various observation conditions have been demonstrated.