Applying Deep Learning to P-Band SAR Tomographic Imaging in Preparation for the Future Biomass Mission

Z. Berenger; L. Denis; F. Tupin; L. Ferro-Famil

doi:10.1109/IGARSS52108.2023.10282400

Applying Deep Learning to P-Band SAR Tomographic Imaging in Preparation for the Future Biomass Mission

Z. Berenger
, L. Denis
, F. Tupin
, L. Ferro-Famil

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 light-weight neural network to dramatically accelerate tomographic reconstruction in anticipation of the deluge of data generated by the future BIOMASS satellite.

Original language	English
Title of host publication	IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	7758-7760
Number of pages	3
ISBN (Electronic)	9798350320107
DOIs	https://doi.org/10.1109/IGARSS52108.2023.10282400
Publication status	Published - 1 Jan 2023
Event	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States Duration: 16 Jul 2023 → 21 Jul 2023

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2023-July

Conference

Conference	2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Country/Territory	United States
City	Pasadena
Period	16/07/23 → 21/07/23

Access to Document

10.1109/IGARSS52108.2023.10282400

Cite this

Berenger, Z., Denis, L., Tupin, F., & Ferro-Famil, L. (2023). Applying Deep Learning to P-Band SAR Tomographic Imaging in Preparation for the Future Biomass Mission. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 7758-7760). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS52108.2023.10282400

Berenger, Z. ; Denis, L. ; Tupin, F. et al. / Applying Deep Learning to P-Band SAR Tomographic Imaging in Preparation for the Future Biomass Mission. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 7758-7760 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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title = "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 light-weight neural network to dramatically accelerate tomographic reconstruction in anticipation of the deluge of data generated by the future BIOMASS satellite.",

author = "Z. Berenger and L. Denis and F. Tupin and L. Ferro-Famil",

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language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

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Berenger, Z, Denis, L, Tupin, F & Ferro-Famil, L 2023, Applying Deep Learning to P-Band SAR Tomographic Imaging in Preparation for the Future Biomass Mission. in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2023-July, Institute of Electrical and Electronics Engineers Inc., pp. 7758-7760, 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, Pasadena, United States, 16/07/23. https://doi.org/10.1109/IGARSS52108.2023.10282400

Applying Deep Learning to P-Band SAR Tomographic Imaging in Preparation for the Future Biomass Mission. / Berenger, Z.; Denis, L.; Tupin, F. et al.
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 7758-7760 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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 light-weight neural network to dramatically accelerate tomographic reconstruction in anticipation of the deluge of data generated by the future BIOMASS satellite.

AB - 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 light-weight neural network to dramatically accelerate tomographic reconstruction in anticipation of the deluge of data generated by the future BIOMASS satellite.

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Berenger Z, Denis L, Tupin F, Ferro-Famil L. Applying Deep Learning to P-Band SAR Tomographic Imaging in Preparation for the Future Biomass Mission. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 7758-7760. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS52108.2023.10282400

Applying Deep Learning to P-Band SAR Tomographic Imaging in Preparation for the Future Biomass Mission

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