An Unsupervised Deep Learning Approach for Monitoring the Snow Facies of the Greenland Ice Sheet with InSAR TanDEM-X Data
Becker Campos A, Rizzoli P, Bueso-Bello JL, Braun M (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 175-180
Conference Proceedings Title: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR
Event location: Munich, DEU
ISBN: 9783800762873
Abstract
An integral part of monitoring ice sheets involves identifying snow facies, which are distinct layers or units within the snowpack. In this work, we explore interferometric synthetic aperture radar (InSAR) TanDEM-X data to monitor the Greenland Ice Sheet snow facies across a decade of observations. We propose a novel, fully unsupervised deep learning method utilizing InSAR features like backscatter, volume decorrelation, and geometric parameters. Furthermore, we address the challenges and caveats of unsupervised approaches for managing different bistatic InSAR acquisition geometries. The proposed approach showcases the potential of bistatic InSAR missions for a long-term monitoring of ice sheet dynamics.
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Germany (DE)How to cite
APA:
Becker Campos, A., Rizzoli, P., Bueso-Bello, J.L., & Braun, M. (2024). An Unsupervised Deep Learning Approach for Monitoring the Snow Facies of the Greenland Ice Sheet with InSAR TanDEM-X Data. In Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR (pp. 175-180). Munich, DEU: Institute of Electrical and Electronics Engineers Inc..
MLA:
Becker Campos, Alexandre, et al. "An Unsupervised Deep Learning Approach for Monitoring the Snow Facies of the Greenland Ice Sheet with InSAR TanDEM-X Data." Proceedings of the 15th European Conference on Synthetic Aperture Radar, EUSAR 2024, Munich, DEU Institute of Electrical and Electronics Engineers Inc., 2024. 175-180.
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