KEYWORDS: Synthetic aperture radar, 3D modeling, 3D image processing, 3D acquisition, Reconstruction algorithms, 3D image reconstruction, Image restoration
Neural Radiance Fields (NeRF) is an emerging technique in the three-dimensional (3D) volumetric representation world due to its ability to learn 3D scenes from sparse two-dimensional (2D) imagery. However, the current implementation focus on electro-optical (EO) representations due to NeRF assumptions with lighting traveling through the scene is absorbed, which is analogous to EO sensor operation. In this work we present a framework for utilizing synthetic aperture radar (SAR) imagery in standard NeRF implementations. Because the physical scattering properties in SAR imagery are markedly different from EO images, we adapt the EO-based transform inputs to equivalent SAR-based parameters. We demonstrate our results on a sample measured SAR dataset with two different 3D SAR reconstruction techniques and demonstrate isotropic scatterer extraction on our sample target. Keyword
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