Combining features to improve oil spill classification in SAR images

Authors:
Darby F. de A. Lopes;Geraldo L. B. Ramalho;Fátima N. S. de Medeiros;Rodrigo C. S. Costa;Regia T. S. Araújo
Affiliations:
Image Processing Research Group, Universidade Federal do Ceara, Fortaleza, CE, Brazil;Image Processing Research Group, Universidade Federal do Ceara, Fortaleza, CE, Brazil;Image Processing Research Group, Universidade Federal do Ceara, Fortaleza, CE, Brazil;Image Processing Research Group, Universidade Federal do Ceara, Fortaleza, CE, Brazil;Image Processing Research Group, Universidade Federal do Ceara, Fortaleza, CE, Brazil
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

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Abstract

As radar backscatter values for oil slicks are very similar to backscatter values for very calm sea areas and other ocean phenomena, dark areas in Synthetic Aperture Radar (SAR) imagery tend to be misinterpreted. In this paper three feature sets are used to identify the oil slicks in SAR images. These images are submitted to different MLP architectures to verify the separability performance over each feature set. This analysis is very suitable for remote sensing of environment applications concerning marine oil pollution. The estimated resulting performance points out which feature set is the best suitable for the suggested application.