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Voinov, Sergey und Krause, Detmar und Schwarz, Egbert (2018) Towards Automated Vessel Detection and Type Recognition from VHR Optical Satellite Images. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Seiten 4823-4826. Institute of Electrical and Electronics Engineers (IEEE). 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018-07-22 - 2018-07-27, Valencia, Spain. doi: 10.1109/IGARSS.2018.8519121. ISBN 978-1-5386-7150-4. ISSN 2153-7003.
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Offizielle URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8519121&isnumber=8517275
Kurzfassung
Vessel detection and type recognition is crucial in any maritime surveillance application. This component aims at preventing or investigating unlawful actions present at sea. Modern very high resolution (VHR) optical satellite sensors are able to capture images with spatial resolution up to 0.3m per pixel, which is sufficient to distinguish ship features such as bridge position, cranes, landing pads and many others and thus possible to differentiate ship types. This paper presents a new method for automatic vessel detection and type recognition based on fusion of deep convolutional neural network architectures (CNN), which has potential for near-real time (NRT) applications.
| elib-URL des Eintrags: | https://elib.dlr.de/123852/ | ||||||||||||||||
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| Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||
| Titel: | Towards Automated Vessel Detection and Type Recognition from VHR Optical Satellite Images | ||||||||||||||||
| Autoren: |
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| Datum: | 5 November 2018 | ||||||||||||||||
| Erschienen in: | IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium | ||||||||||||||||
| Referierte Publikation: | Nein | ||||||||||||||||
| Open Access: | Nein | ||||||||||||||||
| Gold Open Access: | Nein | ||||||||||||||||
| In SCOPUS: | Nein | ||||||||||||||||
| In ISI Web of Science: | Nein | ||||||||||||||||
| DOI: | 10.1109/IGARSS.2018.8519121 | ||||||||||||||||
| Seitenbereich: | Seiten 4823-4826 | ||||||||||||||||
| Verlag: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||||||||||||
| ISSN: | 2153-7003 | ||||||||||||||||
| ISBN: | 978-1-5386-7150-4 | ||||||||||||||||
| Status: | veröffentlicht | ||||||||||||||||
| Stichwörter: | Marine vehicles;Task analysis;Training;Object detection;Satellites;Optical sensors;Convolutional neural networks;optical remote sensing;vessel detection;vessel type recognition;object detection;object classification;convolutional neural networks;CNN;deep learning | ||||||||||||||||
| Veranstaltungstitel: | 2018 IEEE International Geoscience and Remote Sensing Symposium | ||||||||||||||||
| Veranstaltungsort: | Valencia, Spain | ||||||||||||||||
| Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
| Veranstaltungsbeginn: | 22 Juli 2018 | ||||||||||||||||
| Veranstaltungsende: | 27 Juli 2018 | ||||||||||||||||
| 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: | 03 Dez 2018 13:25 | ||||||||||||||||
| Letzte Änderung: | 24 Apr 2024 20:27 |
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