DocumentCode :
66093
Title :
Automatic Estimation of Oil Seep Locations in Synthetic Aperture Radar Images
Author :
Suresh, Gopika ; Melsheimer, Christian ; Korber, Jan-Hendrik ; Bohrmann, Gerhard
Author_Institution :
MARUM: Center for Marine Environ. Sci., Bremen, Germany
Volume :
53
Issue :
8
fYear :
2015
fDate :
Aug. 2015
Firstpage :
4218
Lastpage :
4230
Abstract :
A framework for the automatic detection of natural oil slicks and estimation of their associated oil seeps using synthetic aperture radar (SAR) images is presented, and the methodology used has been explained in detail. The designed detection system is the first automatic oil seep estimation system known to exist. The system detects oil slicks in individual SAR images and estimates their origins on the sea surface. Spatial clustering of temporally recurrent slick origins is conducted in order to estimate the locations of the associated oil seeps on the sea floor. The system is implemented in the programming language Python and a direct rule-based approach is employed for the classification unit. A data set of 178 images of the Black Sea acquired by ENVISAT´s Advanced Synthetic Aperture Radar was used to test the algorithm. In this paper, the methodology used to design the algorithm and the automatically estimated oil seep locations are reported. The efficiency of the system with respect to manual detection is discussed.
Keywords :
geophysical image processing; marine pollution; oil pollution; oils; synthetic aperture radar; Black Sea images; ENVISAT advanced synthetic aperture radar; Python; classification unit; natural oil slick automatic detection; oil seep location automatic estimation; sea floor; spatial clustering; synthetic aperture radar images; Backscatter; Estimation; Feature extraction; Image segmentation; Sea surface; Shape; Synthetic aperture radar; Advanced Synthetic Aperture Radar (ASAR); Automatic detection; ENVISAT; classification; feature extraction; oil seep; oil slick; synthetic aperture radar (SAR);
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2015.2393375
Filename :
7042281
Link To Document :
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