Using Boosting to Improve Oil Spill Detection in SAR Images

Authors:
Geraldo L. B. Ramalho;Fatima N. S. Medeiros
Affiliations:
Universidade Federal do Cear, Brazil;Universidade Federal do Cear, Brazil
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

Citing 0
Cited 2

On the evaluation of texture and color features for nondestructive corrosion detection

EURASIP Journal on Advances in Signal Processing - Special issue on signal processing in advanced nondestructive materials inspection
Improving reliability of oil spill detection systems using boosting for high-level feature selection

ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Marine surveillance system which uses Synthetic Aperture Radar (SAR) images to oil spill detection must minimize false alarms in order to improve its reliability. This paper presents an application that uses boosting method to minimize misclassification and yields better generalization. Different feature sets were applied to neural network classifiers and its performance compared do boosting methods. The experiments reached substantial improvement in the classification accuracy to discriminate oil spots from the look-alike ones.