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An open source approach for oil spill detection using Sentinel-1 SAR images
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Paper Abstract

Marine domain awareness is important for crisis management in case of accidents but also as a surveillance tool against illicit discharge activities. Copernicus Sentinel-1 data are used for the detection of spills as they are provided free and in near real time. The availability of data and the recently developed cloud infrastructures give the opportunity for developing open source algorithms and techniques for oil spill detection. In this study a simple semi-automatic system for oil spill detection is presented for Sentinel-1 images. After image segmentation, the Orfeo Toolbox SVM vector classifier is trained based on the features extracted and separates the objects to possible oil spills and lookalikes. The method has been tested using three images depicting the evolution of an oil spill that resulted from a ship collision in the Mediterranean, offshore Corsica, on the 7th October 2018. Results were sound but further development is needed especially on algorithm training and feature extraction. All the software packages used are free and open source.

Paper Details

Date Published: 27 June 2019
PDF: 6 pages
Proc. SPIE 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), 1117412 (27 June 2019); doi: 10.1117/12.2539256
Show Author Affiliations
Evangelia Efi Konstantinidou, Univ. of the Aegean (Greece)
Polychronis Kolokoussis, National Technical Univ. of Athens (Greece)
Konstantinos Topouzelis, Univ. of the Aegean (Greece)
Ioannis Moutzouris-Sidiris, Univ. of the Aegean (Greece)


Published in SPIE Proceedings Vol. 11174:
Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019)
Kyriacos Themistocleous; Giorgos Papadavid; Silas Michaelides; Vincent Ambrosia; Diofantos G. Hadjimitsis, Editor(s)

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