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.

翻译：高分辨率合成孔径雷达（SAR）图像在复杂场景中的模拟一直是一项重要的研究挑战。微波领域表面散射模型的发展及其可逆性，将在提升SAR图像模拟真实性以及促进目标参数反演方面发挥关键作用。受计算机图形学领域启发，本文提出了一种综合考虑镜面反射和漫反射贡献的微波表面渲染模型。该模型基于基尔霍夫近似（KA）和微扰法（SPM），通过相干空间变化双向散射分布函数（CSVBSDF）进行解析表示，并借助光线追踪与快速映射投影技术的协同组合实现SAR成像。此外，针对CSVBSDF表面散射参数学习，构建了基于SAR图像的可微光线追踪（DRT）引擎。在该SAR图像仿真引擎中，利用可微反向光线追踪技术可从SAR图像快速估计参数梯度。通过仿真实验及与真实SAR图像的对比验证了该方法的有效性。研究表明，通过学习表面散射参数，可显著提升多种观测条件下SAR图像的模拟性能。