Abstract
Oil spill pollution plays a significant role in damaging marine ecosystem. Discharge of oil due to tanker accidents has the most dangerous effects on marine environment. The main waste source is the ship based operational discharges. 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. One major advantage of SAR is that it can generate imagery under all weather conditions. 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’. The features of detected dark spot are then extracted and classified to discriminate oil spills from look-alikes. This paper describes the development of a new approach to SAR oil spill detection using Segmentation method and Artificial Neural Networks (ANN). A SAR-based oil-spill detection process consists of three stages: image segmentation, feature extraction and object recognition (classification) of the segmented objects as oil spills or look-alikes. The image segmentation was performed with Otsu method. Classification has been done using Back Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature parameters. Improved results have been achieved for the discrimination of oil spills and look-alikes.
Highlights
IntroductionA. Akkartal, et al [1] had discussed about the Synthetic Aperture Radar (SAR) satellites as the main data sources to detect the oil spills discharged into the sea with sufficient accuracies
Dark spot detection has been carried out by segmenting the spill area using segmentation operation based on Otsu method
An oil spill detection algorithm was implemented by hybridizing Otsu method and artificial neural network technique
Summary
A. Akkartal, et al [1] had discussed about the Synthetic Aperture Radar (SAR) satellites as the main data sources to detect the oil spills discharged into the sea with sufficient accuracies. The advantages and disadvantages of using different radar images for oil spill detection in Marmara Sea were investigated. Al [2] was proposed an artificial neural network training algorithm which is implemented in MATLAB language. This implementation is focused on the network parameters in order to get the optimal architecture of the network that means
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