Paper Info
Title
Satellite Oil Spill Detection Using Artificial Neural Networks
Abstract
Oil spills represent a major threat to ocean ecosystems and their health. Illicit pollution requires continuous monitoring and satellite remote sensing technology represents an attractive option for operational oil spill detection. Previous studies have shown that active microwave satellite sensors, particularly Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish oil spills from `look-alikes'. This paper describes the development of a new approach to SAR oil spill detection employing two different Artificial Neural Networks (ANN), used in sequence. The first ANN segments a SAR image to identify pixels belonging to candidate oil spill features. A set of statistical feature parameters are then extracted and used to drive a second ANN which classifies objects into oil spills or look-alikes. The proposed algorithm was trained using 97 ERS-2 SAR and ENVSAT ASAR images of individual verified oil spills or/and look-alikes. The algorithm was validated using a large dataset comprising full-swath images and correctly identified 91.6% of reported oil spills and 98.3% of look-alike phenomena. The segmentation stage of the new technique outperformed the established edge detection and adaptive thresholding approaches. An analysis of feature descriptors highlighted the importance of image gradient information in the classification stage.
Year
DOI
Venue
2013
10.1109/JSTARS.2013.2251864
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Keywords
Field
DocType
edge detection,geophysical image processing,image classification,image segmentation,marine pollution,neural nets,oil pollution,remote sensing by radar,synthetic aperture radar,97 ers-2 sar images,envsat asar images,adaptive thresholding approaches,artificial neural networks,image gradient information,ocean ecosystems,satellite oil spill detection,satellite remote sensing technology,statistical feature parameters,‘cleanseanet’,artificial neural network,oil spill detection
Computer vision,Image gradient,Synthetic aperture radar,Edge detection,Remote sensing,Image segmentation,Artificial intelligence,Pixel,Thresholding,Artificial neural network,Contextual image classification,Mathematics
Journal
Volume
Issue
ISSN
6
6
1939-1404
Citations 
PageRank 
References 
15
0.72
10
Authors
3
Name
Order
Citations
PageRank
Singha, S.1194.57
Timothy J. Bellerby2181.21
Trieschmann, O.3151.05