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Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing
IEEE Transactions on Geoscience and Remote Sensing ( IF 7.5 ) Pub Date : 2024-09-12 , DOI: 10.1109/tgrs.2024.3459620
Jiangtao Wei 1 , Yixiang Luomei 1 , Xu Zhang 1 , Feng Xu 1
Affiliation  

The simulation of high-resolution synthetic aperture radar (SAR) imagery in intricate environments remains a formidable challenge. Advancements in reversible microwave-domain surface scattering models are crucial, potentially revolutionizing the fidelity of SAR simulations and streamlining the extraction of target parameters. Drawing inspiration from computer graphics, this article proposes a novel differentiable ray tracing (DRT) approach for microwave rendering and fast SAR imaging. The rendering model utilizes coherent spatially varying (SV) bidirectional scattering distribution function (CSVBSDF) based on the Kirchhoff approximation (KA) and the small perturbation method (SPM), corresponding to specular and diffuse scattering contributions, respectively. SAR imaging is efficiently executed via a fusion of ray tracing (RT) and rapid mapping projection. The innovative DRT reversible engine enables swift estimation of SAR image parameter gradients for direct CSVBSDF surface scattering parameter optimization. The method’s validity is confirmed through comparative analysis with measured SAR images and other methods, demonstrating marked improvements in SAR simulation fidelity across diverse observational scenarios by learning surface scattering parameters.

中文翻译:


使用可微分射线追踪从 SAR 图像中学习表面散射参数



在复杂环境中模拟高分辨率合成孔径雷达 (SAR) 图像仍然是一项艰巨的挑战。可逆微波域表面散射模型的进步至关重要,有可能彻底改变 SAR 模拟的保真度并简化目标参数的提取。本文从计算机图形学中汲取灵感,提出了一种用于微波渲染和快速 SAR 成像的新型可微分光线追踪 (DRT) 方法。渲染模型利用基于基尔霍夫近似 (KA) 和小扰动方法 (SPM) 的相干空间变化 (SV) 双向散射分布函数 (CSVBSDF),分别对应于镜面散射和漫散射贡献。通过光线追踪 (RT) 和快速测绘投影的融合,有效地执行 SAR 成像。创新的 DRT 可逆引擎能够快速估计 SAR 图像参数梯度,以实现直接 CSVBSDF 表面散射参数优化。通过与实测 SAR 图像和其他方法的比较分析,证实了该方法的有效性,证明通过学习表面散射参数,在不同观测场景下 SAR 模拟保真度得到显着提高。
更新日期:2024-09-12
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