Comparative algorithms for oil spill automatic detection using multimode RADARSAT-1 SAR data

This study is utilized comparative algorithms for automatic detection of oil spill from different RADARSAT-1 SAR mode data (Standard beam S2, Wide beam Wl and fine beam F1). In doing so, three algorithms are implemented: Co-occurrence textures; post supervised classification, and neural net work (NN). The study shows that the standard deviation of the estimated error for neural net work of value 0.12 is lower than Entropy and the Mahalanobis algorithms. In conclusion, ANN performed accurately as automatic detection tool for oil spill in RADARSAT data.