Single Target SAR 3D Reconstruction Based on Deep Learning

Abstract

Synthetic aperture radar tomography (TomoSAR) is an important 3D mapping method. Traditional TomoSAR requires a large number of observation orbits however, it is hard to meet the requirement of massive orbits. While on the one hand, this is due to funding constraints, on the other hand, because the target scene is changing over time and each observation orbit consumes lots of time, the number of orbits can be fewer as required within a narrow time window. When the number of observation orbits is insufficient, the signal-to-noise ratio (SNR), peak-to-sidelobe ratio (PSR), and resolution of 3D reconstruction results will decline severely, which seriously limits the practical application of TomoSAR. In order to solve this problem, we propose to use a deep learning network to improve the resolution and SNR of 3D reconstruction results under the condition of very few observation orbits by learning the prior distribution of targets. We use all available orbits to reconstruct a high resolution target, while only very few (around 3) orbits to reconstruct a low resolution input. The low-res and high-res 3D voxel-grid pairs are used to train a 3D super-resolution (SR) CNN (convolutional neural network) model, just like ordinary 2D image SR tasks. Experiments on the Civilian Vehicle Radar dataset show that the proposed deep learning algorithm can effectively improve the reconstruction both in quality and in quantity. In addition, the model also shows good generalization performance for targets not shown in the training set.

Efficient physics-based learned reconstruction methods for real-time 3D near-field MIMO radar imaging

“…Despite this, deep learning-based reconstruction methods have not been studied much in the literature for near-field radar imaging. Most of the proposed methods are for far-field settings in SAR/ISAR or MIMO radar imaging [37,[47][48][49][50][51][52][53][54]. In the near-field and MIMO radar imaging context, there are few works for learning-based approaches [55,56], but to the best of our knowledge, there is no DNN-based reconstruction approach developed and shown to be successful for imaging 3D extended targets with random phase.…”

Iterative Online 3D Reconstruction from RGB Images

“…Reconstruction algorithms have been presented in the literature that operate on various sensor modalities, including LIDAR [3], RADAR [4], RGB-D [5] and monocular (RGB) cameras [6][7][8][9][10][11][12][13]. In this paper, we focus on reconstruction from RGB images, since cameras are an attractive sensor modality for aerial and ground robots because they are more affordable, lighter and power-efficient than active sensor modalities.…”

TomoSAR 3D Reconstruction for Buildings Using Very Few Tracks of Observation: A Conditional Generative Adversarial Network Approach

“…In 2019, Siyan Zhou et al proposed a deep fully connected network to denoise the 3D point cloud data of a single independent building to obtain a relatively flat surface [7]; however, this method cannot handle buildings with overlap and finds it hard to process the point cloud data of a large-scale scene. In 2021, Shihong Wang et al proposed a 3D autoencoder network to filter the low-resolution 3D voxel data generated from three-track circular SAR data to approach the high-resolution results of all tracks [8]. However, this method requires a lot of computation and memory resources to process 3D voxel data, which is of low efficiency.…”