Generating Sentinel-1 WV-Mode Quicklooks via Deep Learning Polarimetric Information Reconstruction

In this study we present some preliminary achievements on our proposed deep learning framework specifically designed to perform Sentinel-1 polarimetric information reconstruction. In particular, we aim at demonstrating our deep learning framework ability to generate missing polarization information...

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Bibliographic Details
Main Authors: De Laurentiis, Leonardo, Albinet, Clement
Format: Conference Proceeding
Language:English
Subjects:
Deep learning
Machine Learning
Measurement
Polarimetry
PSNR
Quicklook
Robustness
Sentinel-1
Statistical analysis
Visualization
Online Access:Request full text
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Description
Summary:In this study we present some preliminary achievements on our proposed deep learning framework specifically designed to perform Sentinel-1 polarimetric information reconstruction. In particular, we aim at demonstrating our deep learning framework ability to generate missing polarization information from single-polarization datasets, with a view to supporting the final generation of useful quicklooks also starting from single-polarization Sentinel-1 data. After a robust validation, the deep learning framework may prove effective in both qualitative and quantitative assessments based on reconstructed quicklooks, notably for the Sentinel-1 Wave (WV) Mode acquisitions, generally acquired and delivered at a single polarization mode, thus with no default colorization scheme for quicklooks.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10640919