Differentiable SAR Renderer and SAR Target Reconstruction

TL;DR

This paper introduces a differentiable SAR renderer that enables end-to-end inverse target reconstruction from SAR images by reformulating SAR imaging as a differentiable process, allowing gradient-based optimization.

Contribution

It develops the first differentiable SAR renderer with analytical gradients, facilitating accurate 3D inverse target reconstruction from SAR data.

Findings

Effective reconstruction of 3D targets demonstrated in simulations.

Method works with both synthetic and real SAR data.

Achieves promising results in target geometry and scattering attribute recovery.

Abstract

Forward modeling of wave scattering and radar imaging mechanisms is the key to information extraction from synthetic aperture radar (SAR) images. Like inverse graphics in optical domain, an inherently-integrated forward-inverse approach would be promising for SAR advanced information retrieval and target reconstruction. This paper presents such an attempt to the inverse graphics for SAR imagery. A differentiable SAR renderer (DSR) is developed which reformulates the mapping and projection algorithm of SAR imaging mechanism in the differentiable form of probability maps. First-order gradients of the proposed DSR are then analytically derived which can be back-propagated from rendered image/silhouette to the target geometry and scattering attributes. A 3D inverse target reconstruction algorithm from SAR images is devised. Several simulation and reconstruction experiments are conducted, including targets with and without background, using both synthesized data or real measured inverse SAR (ISAR) data by ground radar. Results demonstrate the efficacy of the proposed DSR and its inverse approach.