Qiu, Chunping und Schmitt, Michael und Zhu, Xiao Xiang (2019) Fusing Multi-Seasonal Sentinel-2 Images with Residual Convolutional Neural Networks for Local Climate Zone-Derived Urban Land Cover Classification. IGARSS 2019, 2019-07-28 - 2019-08-02, Yokohama/Japan. doi: 10.1109/IGARSS.2019.8898223.
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Offizielle URL: https://ieeexplore.ieee.org/document/8898223
Kurzfassung
This paper proposes a framework to fuse multi-seasonal Sentinel-2 images, with application on LCZ-derived urban land cover classification. Cross-validation over a seven-city study area in central Europe demonstrates its consistently better performance over several previous approaches, with the same experimental setup. Based on our previous work, we can conclude that decision-level fusion is better than feature-level fusion for similar tasks at similar scale with multi-seasonal Sentinel-2 images. With the framework, urban land cover maps of several cities are produced. The visualization of two exemplary areas shows urban structures that are consistent with existing datasets. This framework can be also generally beneficial for other types of urban mapping.
elib-URL des Eintrags: | https://elib.dlr.de/132447/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | Fusing Multi-Seasonal Sentinel-2 Images with Residual Convolutional Neural Networks for Local Climate Zone-Derived Urban Land Cover Classification | ||||||||||||||||
Autoren: |
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Datum: | 2019 | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
DOI: | 10.1109/IGARSS.2019.8898223 | ||||||||||||||||
Seitenbereich: | Seiten 5037-5040 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | Sentinel-2, Classification, residual convolutional neural network (ResNet), urban land cover, long short-term memory (LSTM) | ||||||||||||||||
Veranstaltungstitel: | IGARSS 2019 | ||||||||||||||||
Veranstaltungsort: | Yokohama/Japan | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 28 Juli 2019 | ||||||||||||||||
Veranstaltungsende: | 2 August 2019 | ||||||||||||||||
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 - Vorhaben hochauflösende Fernerkundungsverfahren (alt) | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||
Hinterlegt von: | Hong, Danfeng | ||||||||||||||||
Hinterlegt am: | 06 Dez 2019 16:40 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:35 |
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