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Journal Articles IEEE Geoscience and Remote Sensing Letters Year : 2023

A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas

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 high 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 lightweight neural networks can be trained to perform this inversion with a single feedforward 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.
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Dates and versions

hal-04144659 , version 1 (28-06-2023)

Identifiers

  • HAL Id : hal-04144659 , version 1

Cite

Zoé Berenger, Loïc Denis, Florence Tupin, Laurent Ferro-Famil, Yue Huang. A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas. IEEE Geoscience and Remote Sensing Letters, In press. ⟨hal-04144659⟩
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