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

Evangelia Efi Konstantinidou; Polychronis Kolokoussis; Konstantinos Topouzelis; Ioannis Moutzouris-Sidiris

doi:10.1117/12.2539256

27 June 2019 An open source approach for oil spill detection using Sentinel-1 SAR images

Evangelia Efi Konstantinidou, Polychronis Kolokoussis, Konstantinos Topouzelis, Ioannis Moutzouris-Sidiris

Proceedings Volume 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019); 1117412 (2019) https://doi.org/10.1117/12.2539256
Event: Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), 2019, Paphos, Cyprus

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.

Citation Download Citation

Evangelia Efi Konstantinidou, Polychronis Kolokoussis, Konstantinos Topouzelis, and Ioannis Moutzouris-Sidiris "An open source approach for oil spill detection using Sentinel-1 SAR images", Proc. SPIE 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), 1117412 (27 June 2019); https://doi.org/10.1117/12.2539256

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