Circular SAR Incoherent 3D Imaging with a NeRF-Inspired Method

Abstract

Circular synthetic aperture radar (CSAR) has the potential to form 3D images with single-pass single-channel radar data, which is very time-efficient. This article proposes a volumetric neural renderer that utilizes CSAR 2D amplitude images to reconstruct the 3D power distribution of the imaged scene. The innovations are two-fold: Firstly, we propose a new SAR amplitude image formation model that establishes a linear mapping relationship between multi-look amplitude-squared SAR images and a real-valued 4D (spatial location (x, y, z) and azimuth angle θ) radar scattered field. Secondly, incorporating the proposed image formation model and SAR imaging geometry, we extend the neural radiance field (NeRF) methods to reconstruct the 4D radar scattered field using a set of 2D multi-aspect SAR images. Using real-world drone SAR data, we demonstrate our method for (1) creating realistic SAR imagery from arbitrary new viewpoints and (2) reconstructing high-precision 3D structures of the imaged scene.

Innovative Rotating SAR Mode for 3D Imaging of Buildings

“…Then, based on 12 channels, total baseline length of 3 m, and average flight altitude of 4150 m X-band airborne CFASAR data, Li proposed a wave number domain layered phase compensation method, gaining clear 3D building reconstruction [28]. In the same year, Zhang, Lin, and others introduced the neural radiation field into CSAR image processing and used 55 images with a flight radius of 1000 m as a training set to reconstruct detailed 3D buildings [29].…”

Camera and LiDAR Fusion for Urban Scene Reconstruction and Novel View Synthesis via Voxel-Based Neural Radiance Fields

“…With the emergence of Neural Radiance Fields (NeRF) [15], the research on 3D reconstruction algorithms has rapidly progressed [16]. Many researchers have applied the NeRF model to the field of remote sensing mapping [17,18]. Compared to classic 3D reconstruction methods with explicit geometric representations, NeRF's neural implicit representation is smooth, continuous, differentiable, and capable of better handling complex lighting effects.…”