Robust Classification for Imprecise Environments
Machine Learning
Fractal dimension algorithm for detecting oil spills using RADARSAT-1 SAR
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Specialties [solar wings, oil spill avoidance, on-line patents]
IEEE Spectrum - Special issue: technology 1995
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The main objective of this work is to develop comparative automatic detection procedures for oil spill pixels in multimode (Standard beam S2, Wide beam W1 and fine beam F1) RADARSAT-1 SAR satellite data that were acquired in the Malacca Straits using two algorithms namely, post supervised classification, and neural network (NN) for oil spill detection. The results show that NN is the best indicator for oil spill detection as it can discriminate oil spill from its surrounding such as look-alikes, sea surface and land. The receiver operator characteristic (ROC) is used to determine the accuracy of oil spill detection from RADARSAT-1 SAR data. The results show that oil spills, lookalkies, and sea surface roughness are perfectly discriminated with an area difference of 20% for oil spill, 35% look-alikes, 15% land and 30% for the sea roughness. The NN shows higher performance in automatic detection of oil spill in RADARSAT-1 SAR data as compared to Mahalanobis classification with standard deviation of 0.12. It can therefore be concluded that NN algorithm is an appropriate algorithm for oil spill automatic detection and W1 beam mode is appropriate for oil spill and look-alikes discrimination and detection.