Intelligent System for Feature Extraction of Oil Slick in SAR Images: Speckle Filter Analysis
The development of automatic techniques for oil slick identification on the sea surface, captured through remote sensing images, cause a positive impact to a complete monitoring of the oceans and seas. C-band SAR (ERS-1, ERS-2, Radarsat and Envisat projects) is well adapted to detect ocean pollution because the backscatter is reduced by oil slick. This work propose a system for segmentation and feature extraction of oil slicks candidates based on techniques of digital image processing (filters, gradients, mathematical morphology) and artificial neural network (ANN). Different algorithms of speckle filtering are tested and a comparison for the considered system is presented. The process is thought to possess a level of automatization that minimizes the intervention of a human operator, being possible the processing of larger amount data. The focus of the work is to present a study detailed for feature extraction block proposed (architecture used and computational tools).
KeywordsMathematical Morphology Remote Sensing Symposium Competitive Layer IEEE International Geoscience Large Amount Data
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