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Voinov, Sergey und Heymann, Frank und Bill, Ralf und Schwarz, Egbert (2019) Multiclass Vessel Detection From High Resolution Optical Satellite Images Based On Deep Neural Networks. In: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Seiten 166-169. Institute of Electrical and Electronics Engineers (IEEE). 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019-07-28 - 2019-08-02, Yokohama, Japan. doi: 10.1109/IGARSS.2019.8900506. ISBN 978-1-5386-9154-0. ISSN 2153-7003.
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Offizielle URL: https://ieeexplore.ieee.org/document/8900506
Kurzfassung
One of the core components of remote sensing based maritime surveillance applications is vessel detection. It helps to prevent and investigate different unlawful actions as well as environmental hazards present at sea. Growing constellation of very high resolution (VHR) optical satellite sensors are able to frequently cover large areas with spatial resolution of up to 0.3m per pixel, which is sufficient to detect and distinguish different types of vessels. This paper presents a novel method for automatic multiclass vessel detection with use of deep convolutional neural networks (DCNN) and principle component analysis (PCA). The described approach provides reasonable performance and therefore is potentially suitable for near real time (NRT) applications.
| elib-URL des Eintrags: | https://elib.dlr.de/131798/ | ||||||||||||||||||||
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| Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||
| Titel: | Multiclass Vessel Detection From High Resolution Optical Satellite Images Based On Deep Neural Networks | ||||||||||||||||||||
| Autoren: |
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| Datum: | 14 November 2019 | ||||||||||||||||||||
| Erschienen in: | IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium | ||||||||||||||||||||
| Referierte Publikation: | Ja | ||||||||||||||||||||
| Open Access: | Nein | ||||||||||||||||||||
| Gold Open Access: | Nein | ||||||||||||||||||||
| In SCOPUS: | Nein | ||||||||||||||||||||
| In ISI Web of Science: | Nein | ||||||||||||||||||||
| DOI: | 10.1109/IGARSS.2019.8900506 | ||||||||||||||||||||
| Seitenbereich: | Seiten 166-169 | ||||||||||||||||||||
| Verlag: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||||||||||||||||
| ISSN: | 2153-7003 | ||||||||||||||||||||
| ISBN: | 978-1-5386-9154-0 | ||||||||||||||||||||
| Status: | veröffentlicht | ||||||||||||||||||||
| Stichwörter: | optical remote sensing, multiclass vessel detection, ship detection,vesselclassification, object detection, convolutional neural networks, CNN, deep learning | ||||||||||||||||||||
| Veranstaltungstitel: | 2019 IEEE International Geoscience and Remote Sensing Symposium | ||||||||||||||||||||
| Veranstaltungsort: | Yokohama, Japan | ||||||||||||||||||||
| Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||
| Veranstaltungsbeginn: | 28 Juli 2019 | ||||||||||||||||||||
| Veranstaltungsende: | 2 August 2019 | ||||||||||||||||||||
| Veranstalter : | IEEE GRSS | ||||||||||||||||||||
| 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 - Geoprodukte u. - Systeme, Services | ||||||||||||||||||||
| Standort: | Neustrelitz | ||||||||||||||||||||
| Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Nationales Bodensegment | ||||||||||||||||||||
| Hinterlegt von: | Voinov, Sergey | ||||||||||||||||||||
| Hinterlegt am: | 02 Dez 2019 11:59 | ||||||||||||||||||||
| Letzte Änderung: | 24 Apr 2024 20:35 |
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