Deep-Learning-Based Approach in Imaging Radiometry by Aperture Synthesis: An Alias-Free Method-Reference-Cited by-同舟云学术

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Deep-Learning-Based Approach in Imaging Radiometry by Aperture Synthesis: An Alias-Free Method

Published:2024 Issue: Volume:17 Page:6693-6711
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Faucheron Richard¹^ORCID,Anterrieu Eric¹^ORCID,Yu Louise²^ORCID,Khazaal Ali¹^ORCID,Rodríguez-Fernández Nemesio J.¹^ORCID

Affiliation:

1. Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, Centre National D'Etudes Spatiales, Centre National de la Recherche Scientifique, Institut de Recherche pour le Développement, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Toulouse, France

2. Centre National D'Etudes Spatiales, Toulouse, France

Funder

Centre National d'Etudes Spatiales

Research and Technology program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10461331.pdf?arnumber=10461331

Reference29 articles.

1. Deep learning

2. Microwave SAIR Imaging Approach Based on Deep Convolutional Neural Network

3. Non-Uniform Synthetic Aperture Radiometer Image Reconstruction Based on Deep Convolutional Neural Network

4. Deep Learning Imaging for 1-D Aperture Synthesis Radiometers

5. Improved image reconstruction techniques for synthetic aperture radiometers

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