Oil spill feature selection and classification using decision tree forest on SAR image data

A novel oil spill feature selection and classification technique is presented, based on a forest of decision trees. The parameters of the two-class classification problem of oil spills and look-alikes are explored. The contribution to the final classification of the 25 most commonly used features in the scientific community was examined. The work is sought in the framework of a multi-objective problem, i.e. the minimization of the used input features and, at the same time, the maximization of the overall testing classification accuracy. Results showed that the optimum forest contains 70 trees and the three most important combinations contain 4, 6 and 9 features. The latter feature combination can be seen as the most appropriate solution of the decision forest study. Examination of the robustness of the above result showed that the proposed combination achieved higher classification accuracy than other well-known statistical separation indexes. Moreover, comparisons with previous findings converge on the classification accuracy (up to 84.5%) and to the number of selected features, but diverge on the actual features. This observation leads to the conclusion that there is not a single optimum feature combination; several sets of combinations exist which contain at least some critical features.