Tubbesing, Raphael und Warmedinger, Leena Julia und Huber, Martin und Roth, Achim und Wessel, Birgit (2022) Automatic ML-based water body detection as part of the hydrological conditioning of the TanDEM-X DEM. In: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR, Seiten 700-705. VDE Publishing House. EUSAR 2022, 2022-07-25 - 2022-07-27, Leipzig, Deutschland. ISSN 2197-4403.
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Kurzfassung
The TanDEM-X mission provides a global digital elevation model (DEM) with high spatial resolution and therefore is able to capture the local geomorphic appearance of the world’s surface. In particular, the high quality and homogeneity enable new possibilities for hydrologic products, where the earth’s relief is a key source of information. As the TanDEMX DEM is provided in an unedited version some distortions are still present and require pre-processing. In particular, decorrelation effects at open water areas cause a noisy and rough appearance of naturally flat surfaces, which impair hydrological assessments. In this paper an automated global water body classification is presented, utilizing TanDEM-X synthetic aperture radar, optical and terrain data to generate a water mask tailored towards these areas in the DEM. The generated water mask is a vital product for the hydrologic conditioning. The state-of-the-art Gradient Boosted Decision Trees is chosen as the underlying classifier. It is combined with a Bayesian hyperparameter optimization to avoid manual tuning. For the generation of training data, different global water masks are evaluated and appropriate training pixels are identified. First results show the potential of this approach and particularly the advantage in detecting water north of 60° degrees, when using the lowest Amplitude measured in the feature space.
elib-URL des Eintrags: | https://elib.dlr.de/188538/ | ||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||||||
Titel: | Automatic ML-based water body detection as part of the hydrological conditioning of the TanDEM-X DEM. | ||||||||||||||||||||||||
Autoren: |
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Datum: | 18 Mai 2022 | ||||||||||||||||||||||||
Erschienen in: | Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||
Seitenbereich: | Seiten 700-705 | ||||||||||||||||||||||||
Verlag: | VDE Publishing House | ||||||||||||||||||||||||
ISSN: | 2197-4403 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | machine learning, TanDEM-X, Gradient Boosted Decision Trees, hydrology, SAR, water body detection, DEM | ||||||||||||||||||||||||
Veranstaltungstitel: | EUSAR 2022 | ||||||||||||||||||||||||
Veranstaltungsort: | Leipzig, Deutschland | ||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||
Veranstaltungsbeginn: | 25 Juli 2022 | ||||||||||||||||||||||||
Veranstaltungsende: | 27 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 - Fernerkundung u. Geoforschung | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche | ||||||||||||||||||||||||
Hinterlegt von: | Tubbesing, Raphael | ||||||||||||||||||||||||
Hinterlegt am: | 27 Sep 2022 14:28 | ||||||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:49 |
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