Guo, Jianhua und Xu, Qingsong und Zeng, Yue und Liu, Zhiheng und Zhu, Xiao Xiang (2022) Semi-Supervised Cloud Detection in Satellite Images by Considering the Domain Shift Problem. Remote Sensing, 14, Seite 2641. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs14112641. ISSN 2072-4292.
PDF
- Verlagsversion (veröffentlichte Fassung)
20MB |
Offizielle URL: https://www.mdpi.com/2072-4292/14/11/2641
Kurzfassung
In terms of semi-supervised cloud detection work, efforts are being made to learn a promising cloud detection model via a limited number of pixel-wise labeled images and a large number of unlabeled ones. However, remote sensing images obtained from the same satellite sensor often show a data distribution drift problem due to the different cloud shapes and land-cover types on the Earth’s surface. Therefore, there are domain distribution gaps between labeled and unlabeled satellite images. To solve this problem, we take the domain shift problem into account for the semi-supervised learning (SSL) network. Feature-level and output-level domain adaptations are applied to reduce the domain distribution gaps between labeled and unlabeled images, thus improving predicted results accuracy of the SSL network. Experimental results on Landsat-8 OLI and GF-1 WFV multispectral images demonstrate that the proposed semi-supervised cloud detection network (SSCDnet) is able to achieve promising cloud detection performance when using a limited number of labeled samples and outperforms several state-of-the-art SSL methods.
elib-URL des Eintrags: | https://elib.dlr.de/192693/ | ||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||
Titel: | Semi-Supervised Cloud Detection in Satellite Images by Considering the Domain Shift Problem | ||||||||||||||||||||||||
Autoren: |
| ||||||||||||||||||||||||
Datum: | Mai 2022 | ||||||||||||||||||||||||
Erschienen in: | Remote Sensing | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||
Band: | 14 | ||||||||||||||||||||||||
DOI: | 10.3390/rs14112641 | ||||||||||||||||||||||||
Seitenbereich: | Seite 2641 | ||||||||||||||||||||||||
Verlag: | Multidisciplinary Digital Publishing Institute (MDPI) | ||||||||||||||||||||||||
ISSN: | 2072-4292 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | remote sensing imagery; cloud detection; semi-supervised learning; distribution drift; domain shift problem; domain adaptation | ||||||||||||||||||||||||
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 | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||||||
Hinterlegt von: | Haschberger, Dr.-Ing. Peter | ||||||||||||||||||||||||
Hinterlegt am: | 20 Dez 2022 11:00 | ||||||||||||||||||||||||
Letzte Änderung: | 19 Okt 2023 13:22 |
Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags