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Proceedings Paper

Oil spill detection: SAR multiscale segmentation and object features evaluation
Author(s): Kostas Topouzelis; Vassillia Karathanassi; Petros Pavlakis; Dimitris Rokos
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Paper Abstract

The use of image segmentation and object feature extraction in order to classify SAR image objects into oil spill or other features (oil slick look-alikes), it is widely acceptable in oil spill detection research. For this purpose, a number of features (geometric, surrounding, backscattering, etc.) are usually calculated and introduced in a decision support procedure. The aim of the present study is presentation, analysis and evaluation of the above features in order to produce general rules adequate to identify oil spills in any SAR image. SAR image processing is based on a new multi-segmentation technique. As a first step, image objects in different scales are extracted using the multi-segmentation procedure. Following segmentation, a hierarchical network of image objects is developed, which simultaneously presents object information and fuzzy rules for classification. In experiments implemented in SAR images, the method developed has successfully detected oil spills and look alikes. Texture behavior most contributes to detection (texture characteristics 80%), followed by physical behavior (actual backscatter characteristics 53%, spot surroundings 26 %) and finally geometry behavior (geometrical characteristics 2%).

Paper Details

Date Published: 14 February 2003
PDF: 11 pages
Proc. SPIE 4880, Remote Sensing of the Ocean and Sea Ice 2002, (14 February 2003); doi: 10.1117/12.462518
Show Author Affiliations
Kostas Topouzelis, National Technical Univ. of Athens (Greece)
Vassillia Karathanassi, National Technical Univ. of Athens (Greece)
Petros Pavlakis, National Technical Univ. of Athens (Greece)
Dimitris Rokos, National Technical Univ. of Athens (Greece)

Published in SPIE Proceedings Vol. 4880:
Remote Sensing of the Ocean and Sea Ice 2002
Charles R. Bostater; Rosalia Santoleri, Editor(s)

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