A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas

• URL: http://arxiv.org/abs/2301.08605v1
• Date: Fri, 20 Jan 2023 14:34:03 GMT
• Title: A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas
• Authors: Zo\'e Berenger, Lo\"ic Denis, Florence Tupin, Laurent Ferro-Famil, Yue Huang
• Abstract summary: We show that light-weight neural networks can be trained to perform the tomographic inversion with a single feed-forward pass. We train our encoder-decoder network using simulated data and validate our technique on real L-band and P-band data.
• Score: 10.477070348391079
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Synthetic aperture radar tomographic imaging reconstructs the three-dimensional reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration. In forest areas, a large number of elements backscatter the radar signal within each resolution cell. To reconstruct the vertical reflectivity profile, state-of-the-art techniques perform a regularized inversion implemented in the form of iterative minimization algorithms. We show that light-weight neural networks can be trained to perform the tomographic inversion with a single feed-forward pass, leading to fast reconstructions that could better scale to the amount of data provided by the future BIOMASS mission. We train our encoder-decoder network using simulated data and validate our technique on real L-band and P-band data.

Related papers

• Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors [52.195637608631955]
  Non-line-of-sight (NLOS) imaging has attracted increasing attention due to its potential applications. Existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors. We introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF)
  arXiv  Detail & Related papers  (2024-09-21T04:39:45Z)
• Sparse Multi-baseline SAR Cross-modal 3D Reconstruction of Vehicle Targets [5.6680936716261705]
  We propose a Cross-Modal Reconstruction Network (CMR-Net), which integrates differentiable render and cross-modal supervision with optical images. CMR-Net, trained solely on simulated data, demonstrates high-resolution reconstruction capabilities on both publicly available simulation datasets and real measured datasets.
  arXiv  Detail & Related papers  (2024-06-06T15:18:59Z)
• ResFields: Residual Neural Fields for Spatiotemporal Signals [61.44420761752655]
  ResFields is a novel class of networks specifically designed to effectively represent complex temporal signals. We conduct comprehensive analysis of the properties of ResFields and propose a matrix factorization technique to reduce the number of trainable parameters. We demonstrate the practical utility of ResFields by showcasing its effectiveness in capturing dynamic 3D scenes from sparse RGBD cameras.
  arXiv  Detail & Related papers  (2023-09-06T16:59:36Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• DH-GAN: A Physics-driven Untrained Generative Adversarial Network for 3D Microscopic Imaging using Digital Holography [3.4635026053111484]
  Digital holography is a 3D imaging technique by emitting a laser beam with a plane wavefront to an object and measuring the intensity of the diffracted waveform, called holograms. Recently, deep learning (DL) methods have been used for more accurate holographic processing. We propose a new DL architecture based on generative adversarial networks that uses a discriminative network for realizing a semantic measure for reconstruction quality.
  arXiv  Detail & Related papers  (2022-05-25T17:13:45Z)
• Deep Domain Adversarial Adaptation for Photon-efficient Imaging Based on Spatiotemporal Inception Network [11.58898808789911]
  In single-photon LiDAR, photon-efficient imaging captures the 3D structure of a scene by only several signal detected per pixel. Existing deep learning models for this task are trained on simulated datasets, which poses the domain shift challenge when applied to realistic scenarios. We propose a network (STIN) for photon-efficient imaging, which is able to precisely predict the depth from a sparse and high-noise photon counting histogram by fully exploiting spatial and temporal information.
  arXiv  Detail & Related papers  (2022-01-07T14:51:48Z)
• MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo [52.329580781898116]
  We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that consider per-scene optimization on densely captured images, we propose a generic deep neural network that can reconstruct radiance fields from only three nearby input views via fast network inference.
  arXiv  Detail & Related papers  (2021-03-29T13:15:23Z)
• Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications [78.63280020581662]
  A novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views. We demonstrate the high performance and robustness of the proposed framework compared with state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-03-24T08:16:32Z)
• Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images [59.906948203578544]
  We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object. We first estimate per-view depth maps using a deep multi-view stereo network. These depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture.
  arXiv  Detail & Related papers  (2020-03-27T21:28:54Z)
• Deep Non-Line-of-Sight Reconstruction [18.38481917675749]
  In this paper, we employ convolutional feed-forward networks for solving the reconstruction problem efficiently. We devise a tailored autoencoder architecture, trained end-to-end reconstruction maps transient images directly to a depth map representation. We demonstrate that our feed-forward network, even though it is trained solely on synthetic data, generalizes to measured data from SPAD sensors and is able to obtain results that are competitive with model-based reconstruction methods.
  arXiv  Detail & Related papers  (2020-01-24T16:05:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.