Learning Surface Scattering Parameters From SAR Images Using
  Differentiable Ray Tracing
Jiangtao Wei Yixiang Luomei Xu Zhang Feng Xu
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.