Title: SAR Ship Detection and Self-Reporting Data Fusion based on Traffic Knowledge
Authors: MAZZARELLA FABIOVESPE MICHELESANTAMARIA SERNA CARLOS
Citation: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS vol. 12 no. 8 p. 1685-1689
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Year: 2015
JRC N°: JRC92959
ISSN: 1545-598X
URI: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7093130
http://publications.jrc.ec.europa.eu/repository/handle/JRC92959
DOI: 10.1109/LGRS.2015.2419371
Type: Articles in periodicals and books
Abstract: The improvement in Maritime Situational Awareness, the capability of understanding events, circumstances, and activities within and impacting the maritime environment, is nowadays of paramount importance for safety and security. The integration of spaceborne synthetic aperture radar (SAR) data and automatic identification system (AIS) information has the appealing potential to provide a better picture of what is happening at sea by detecting vessels that are not reporting their positioning data or, on the other side, by validating ships detected in satellite imagery. In this letter, we propose a novel architecture that is able to increase the quality of SAR/AIS fusion by exploiting knowledge of historical vessel positioning information. Experimental results are presented, testing the algorithm in the specific area of Dover Strait using real SAR and AIS data.
JRC Directorate:Space, Security and Migration

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