Optimum Features Selection for oil Spill Detection in SAR Image

In order to classify the dark objects observed in SAR images into oil spills or lookalikes, many features need to be extracted from these images. In this paper, an algorithm is presented for selecting an optimum set of features from SAR images; which maximizes the discrimination between oil spills and their lookalikes in such images. The proposed algorithm consists of the following sections: detection of dark spots in SAR images, extraction of features, selection of features, and the classification of dark spots into oil spills or lookalikes. It is observed that the proposed algorithm can accurately detect and classify the dark spots in SAR images. In extracting the features, 74 different kinds of features consisting of 32 textural features, 19 geometrical features, 19 physical features and 4 contextual features are extracted. In the feature selection step, eight different evolutionary algorithms are employed to yield the desired feature subsets. The obtained subsets are then evaluated based on the classification error rate criterion; while Bayesian network is used to classify the dark spots into oil spills or lookalikes. The proposed algorithm is applied to a data set of 134 oil spills and 118 lookalikes. The classification rate obtained by using the optimum set of features is 93.19 %.