A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914670774845440 |
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| author | Berenger, Zoé Denis, Loïc Tupin, Florence Ferro-Famil, Laurent Huang, Yue |
| author_facet | Berenger, Zoé Denis, Loïc Tupin, Florence Ferro-Famil, Laurent Huang, Yue |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_08605 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas Berenger, Zoé Denis, Loïc Tupin, Florence Ferro-Famil, Laurent Huang, Yue Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2301.08605 |