Marmanis, Dimitrios und Datcu, Mihai und Esch, Thomas und Stilla, Uwe (2016) Deep Learning Earth Observation Classification Using ImageNet Pre-trained Networks. IEEE Geoscience and Remote Sensing Letters, 13 (1), Seiten 105-109. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2015.2499239. ISSN 1545-598X.
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Offizielle URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7342907
Kurzfassung
Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using CNNs along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained CNN designed for tackling an entirely different classification problem, namely, the ImageNet challenge, and exploit it to extract an initial set of representations. The derived representations are then transferred into a supervised CNN classifier, along with their class labels, effectively training the system. Through this two-stage framework, we successfully deal with the limited-data problem in an end-to-end processing scheme. Comparative results over the UC Merced Land Use benchmark prove that our method significantly outperforms the previously best stated results, improving the overall accuracy from 83.1% up to 92.4%. Apart from statistical improvements, our method introduces a novel feature fusion algorithm that effectively tackles the large data dimensionality by using a simple and computationally efficient approach.
elib-URL des Eintrags: | https://elib.dlr.de/99809/ | ||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||
Titel: | Deep Learning Earth Observation Classification Using ImageNet Pre-trained Networks | ||||||||||||||||||||
Autoren: |
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Datum: | Januar 2016 | ||||||||||||||||||||
Erschienen in: | IEEE Geoscience and Remote Sensing Letters | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||
Band: | 13 | ||||||||||||||||||||
DOI: | 10.1109/LGRS.2015.2499239 | ||||||||||||||||||||
Seitenbereich: | Seiten 105-109 | ||||||||||||||||||||
Herausgeber: |
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Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||
ISSN: | 1545-598X | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | deep learning, classification, pre-trained networks | ||||||||||||||||||||
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 Fernerkundung der Landoberfläche (alt) | ||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Landoberfläche Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse | ||||||||||||||||||||
Hinterlegt von: | Adam, Fathalrahman | ||||||||||||||||||||
Hinterlegt am: | 02 Dez 2015 12:40 | ||||||||||||||||||||
Letzte Änderung: | 28 Nov 2023 08:36 |
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