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Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing

Jiangtao Wei, Yixiang Luomei, Xu Zhang, Feng Xu

TL;DR

This work tackles the challenge of high-fidelity SAR image simulation in complex scenes and the inverse problem of recovering surface scattering parameters. It introduces a forward differentiable ray-tracing framework (DRT) that integrates a surface microwave rendering model based on a hybrid KA+SPM CSVBSDF, enabling gradient-based learning of spatially varying scattering parameters from SAR data. The key contributions include (i) a physics-grounded surface microwave rendering model, (ii) a differentiable SAR renderer with differentiable projection, and (iii) demonstrations that learned CSVBSDF parameters yield more accurate SAR imagery under varying viewing conditions, validated with both simulated and measured data. The approach enables efficient parameter inversion for large-scale scenes and offers a path toward physically interpretable, differentiable SAR simulation with practical impact for design, analysis, and scene understanding.

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.

Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing

TL;DR

This work tackles the challenge of high-fidelity SAR image simulation in complex scenes and the inverse problem of recovering surface scattering parameters. It introduces a forward differentiable ray-tracing framework (DRT) that integrates a surface microwave rendering model based on a hybrid KA+SPM CSVBSDF, enabling gradient-based learning of spatially varying scattering parameters from SAR data. The key contributions include (i) a physics-grounded surface microwave rendering model, (ii) a differentiable SAR renderer with differentiable projection, and (iii) demonstrations that learned CSVBSDF parameters yield more accurate SAR imagery under varying viewing conditions, validated with both simulated and measured data. The approach enables efficient parameter inversion for large-scale scenes and offers a path toward physically interpretable, differentiable SAR simulation with practical impact for design, analysis, and scene understanding.

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.
Paper Structure (25 sections, 39 equations, 16 figures, 7 tables, 1 algorithm)

This paper contains 25 sections, 39 equations, 16 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Traditional computational electromagnetics and imaging methods
  3. Data-based methods
  4. Differential simulation engine
  5. Forward Model
  6. Optical reflection model
  7. Surface microwave rendering model
  8. Surface microwave rendering model
  9. Surface microwave specular BSDF
  10. Surface microwave diffuse BSDF
  11. Surface Microwave BSDF
  12. Ray-mesh intersection and surface parameters mapping
  13. Mapping and projection for fast SAR imaging
  14. Differentiable Ray Tracing
  15. Gradient descent parameter learning
  16. ...and 10 more sections

Figures (16)

  • Figure 1: SAR forward model and backward differentiable learning framework.
  • Figure 2: Triangular rough microfacet scattering geometry diagram.
  • Figure 3: Changes in scattering coefficient caused by different surface scattering parameters of SPM model.
  • Figure 4: Scattering coefficient fusion of double-scale model.
  • Figure 5: Ray intersects triangle surface element.
  • ...and 11 more figures