A novel approach to oil-spill classification, based on the paradigm of one-class classification, is proposed. Basically, a classifier is trained using only examples of oil-spills, instead of using oil-spills and look-alikes, as in two-class approaches. In addition, as a large number of candidate features have been considered in the literature, a feature selection algorithm, to objectively select the most effective subset, is proposed. Results on two case study datasets are reported to validate the proposed approach.
On the Mathematical Formulation of the SAR Oil-Spill Observation Problem
MIGLIACCIO, Maurizio
2008-01-01
Abstract
A novel approach to oil-spill classification, based on the paradigm of one-class classification, is proposed. Basically, a classifier is trained using only examples of oil-spills, instead of using oil-spills and look-alikes, as in two-class approaches. In addition, as a large number of candidate features have been considered in the literature, a feature selection algorithm, to objectively select the most effective subset, is proposed. Results on two case study datasets are reported to validate the proposed approach.File in questo prodotto:
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