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Conference Papers Year : 2023

APPLYING DEEP LEARNING TO P-BAND SAR TOMOGRAPHIC IMAGING IN PREPARATION FOR THE FUTURE BIOMASS MISSION

Abstract

With Synthetic Aperture Radar tomography, it is possible to reconstruct reflectivity profiles in the direction orthogonal to the line-of-sight. When only a small number of interferometric baselines is available, the spatial resolution of profiles produced by beamforming is insufficient. While many iterative algorithms have been proposed in the past years to achieve improved tomographic reconstructions, these methods often require a large computational cost. In this paper we explore the use of a lightweight neural network to dramatically accelerate tomographic reconstruction in anticipation of the deluge of data generated by the future BIOMASS satellite.
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Dates and versions

hal-04152892 , version 1 (05-07-2023)

Identifiers

  • HAL Id : hal-04152892 , version 1

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Zoé Berenger, Loïc Denis, Florence Tupin, Laurent Ferro-Famil. APPLYING DEEP LEARNING TO P-BAND SAR TOMOGRAPHIC IMAGING IN PREPARATION FOR THE FUTURE BIOMASS MISSION. IGARSS, Jul 2023, Pasadena, United States. ⟨hal-04152892⟩
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