Improving reliability of oil spill detection systems using boosting for high-level feature selection

Authors:
Geraldo L. B. Ramalho;Fátima N. S. de Medeiros
Affiliations:
Image Processing Research Group, Federal University of Ceará, Fortaleza, Brazil;Image Processing Research Group, Federal University of Ceará, Fortaleza, Brazil
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

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Abstract

A major problem in surveillance systems is the occurrence of false alarms which lead people to take wrong actions. Thus, if the false alarm is frequent and occurs mainly due to system misclassification, this system will turn into an unreliable one and briefly out of use. This paper proposes a classification method to oil spill detection using SAR images. The proposed methodology uses boosting method to minimize misclassification and also reach better generalization in order to reduce false alarms. Different feature sets were applied to single neural network classifiers and its performance were compared to a modified boosting method which provides a high-level feature selection. The experiments show substantial improvement in discriminating SAR images containing oil spots from the look-alike ones.