%0 Book
%T Deep learning-based vessel detection from very high and medium resolution optical satellite images as component of maritime surveillance systems
%A Voinov, Sergey
%D 2020
%C Rostock
%C Universität Rostock
%G English
%F 1743811403
%O vorgelegt von Sergey Voinov
%O GutachterInnen: Ralf Bill (Universität Rostock, Agrar- und Umweltwissenschaftliche Fakultät, Geodäsie und Geoinformatik) ; Frank Heymann (Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Solar-Terrestrische Physik) ; Peter Reinartz (Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Methodik der Fernerkundung, Photogrammetrie und Bildanalyse)
%O Dissertation Universität Rostock 2020
%X This thesis presents an end-to-end multiclass vessel detection method from optical satellite images. The proposed workflow covers the complete processing chain and involves rapid image enhancement techniques, the fusion with automatic identification system (AIS) data, and the detection algorithm based on convolutional neural networks (CNN). The algorithms presented are implemented in the form of independent software processors and integrated in an automated processing chain as part of the Earth Observation Maritime Surveillance System (EO-MARISS).<eng>
%L 004
%9 theses
%9 Text
%9 Hochschulschrift
%R 10.18453/rosdok_id00002876
%U http://purl.uni-rostock.de/rosdok/id00002876
%U https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00002876-8
%U https://d-nb.info/1293661988/34
%U https://doi.org/10.18453/rosdok_id00002876