Ristea, Nicolae-Catalin und Anghel, Andrei und Datcu, Mihai und Chapron, Betrand (2022) Guided Deep Learning by Subaperture Decomposition: Ocean Patterns from SAR Imagery. In: International Geoscience and Remote Sensing Symposium (IGARSS), Seiten 6825-6828. IEEE - Institute of Electrical and Electronics Engineers. IGARSS 2022, 2022-07-17 - 2022-07-22, Kuala Lumpur, Malaysia. doi: 10.1109/IGARSS46834.2022.9884291.
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Offizielle URL: https://ieeexplore.ieee.org/document/9884291
Kurzfassung
Spaceborne synthetic aperture radar (SAR) can provide meters-scale images of the ocean surface roughness day-or-night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Sentinel-l SAR wave mode (WV) vignettes have made possible to capture many important oceanic and atmospheric phenomena since 2014. However, considering the amount of data provided, expanding applications requires a strategy to automatically process and extract geophysical parameters. In this study, we propose to apply subaperture decomposition (SD) as a preprocessing stage for SAR deep learning models. Our data-centring approach surpassed the baseline by 0.7%, obtaining state-of-the-art on the TenGeoP-SARwv data set. In addition, we empirically showed that SD could bring additional information over the original vignette, by rising the number of clusters for an unsupervised segmentation method. Overall, we encourage the development of data-centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models.
elib-URL des Eintrags: | https://elib.dlr.de/193340/ | ||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||
Titel: | Guided Deep Learning by Subaperture Decomposition: Ocean Patterns from SAR Imagery | ||||||||||||||||||||
Autoren: |
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Datum: | 2022 | ||||||||||||||||||||
Erschienen in: | International Geoscience and Remote Sensing Symposium (IGARSS) | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||
DOI: | 10.1109/IGARSS46834.2022.9884291 | ||||||||||||||||||||
Seitenbereich: | Seiten 6825-6828 | ||||||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | Subapertures decomposition, remote sensing, SAR, deep learning, unsupervised segmentation | ||||||||||||||||||||
Veranstaltungstitel: | IGARSS 2022 | ||||||||||||||||||||
Veranstaltungsort: | Kuala Lumpur, Malaysia | ||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||
Veranstaltungsbeginn: | 17 Juli 2022 | ||||||||||||||||||||
Veranstaltungsende: | 22 Juli 2022 | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz, R - SAR-Methoden | ||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||
Hinterlegt von: | Haschberger, Dr.-Ing. Peter | ||||||||||||||||||||
Hinterlegt am: | 16 Jan 2023 08:56 | ||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:54 |
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