Zhu, Xiao Xiang und Tuia, Devis und Mou, Lichao und Xia, Gui-Song und Zhang, Liangpei und Xu, Feng und Fraundorfer, Friedrich (2017) Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources. IEEE Geoscience and Remote Sensing Magazine (GRSM), 5 (4), Seiten 8-36. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/MGRS.2017.2762307. ISSN 2168-6831.
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Offizielle URL: http://ieeexplore.ieee.org/document/8113128/
Kurzfassung
Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.
elib-URL des Eintrags: | https://elib.dlr.de/118694/ | ||||||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||||||
Titel: | Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources | ||||||||||||||||||||||||||||||||
Autoren: |
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Datum: | Dezember 2017 | ||||||||||||||||||||||||||||||||
Erschienen in: | IEEE Geoscience and Remote Sensing Magazine (GRSM) | ||||||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||||||
Band: | 5 | ||||||||||||||||||||||||||||||||
DOI: | 10.1109/MGRS.2017.2762307 | ||||||||||||||||||||||||||||||||
Seitenbereich: | Seiten 8-36 | ||||||||||||||||||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||||||||||||||
ISSN: | 2168-6831 | ||||||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||||||
Stichwörter: | Deep learning, remote sensing | ||||||||||||||||||||||||||||||||
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 > SAR-Signalverarbeitung | ||||||||||||||||||||||||||||||||
Hinterlegt von: | Mou, LiChao | ||||||||||||||||||||||||||||||||
Hinterlegt am: | 08 Feb 2018 11:42 | ||||||||||||||||||||||||||||||||
Letzte Änderung: | 27 Nov 2023 11:55 |
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