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EnglishA 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.
| Titolo: | On the Mathematical Formulation of the SAR Oil-Spill Observation Problem | |
| Autori: | ||
| Data di pubblicazione: | 2008 | |
| 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. | |
| Handle: | http://hdl.handle.net/11367/25798 | |
| ISBN: | 978-1-4244-2807-6 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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