Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 1...
Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data
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Basel: MDPI AG
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English
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Basel: MDPI AG
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This paper intends to find a more cost-effective way for training oil spill classification systems by introducing active learning (AL) and exploring its potential, so that satisfying classifiers could be learned with reduced number of labeled samples. The dataset used has 143 oil spills and 124 look-alikes from 198 RADARSAT images covering the east...
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Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data
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TN_cdi_doaj_primary_oai_doaj_org_article_3822b24f2fb6473396a01d23907c0e72
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_3822b24f2fb6473396a01d23907c0e72
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2072-4292
E-ISSN
2072-4292
DOI
10.3390/rs9101041
