{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T17:59:25Z","timestamp":1783360765206,"version":"3.54.6"},"reference-count":86,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Linear discriminant analysis (LDA) is a mathematically robust multivariate data analysis approach that is sometimes used for surface oil slick signature classification. Our goal is to rank the effectiveness of LDAs to differentiate oil spills from look-alike slicks. We explored multiple combinations of (i) variables (size information, Meteorological-Oceanographic (metoc), geo-location parameters) and (ii) data transformations (non-transformed, cube root, log10). Active and passive satellite-based measurements of RADARSAT, QuikSCAT, AVHRR, SeaWiFS, and MODIS were used. Results from two experiments are reported and discussed: (i) an investigation of 60 combinations of several attributes subjected to the same data transformation and (ii) a survey of 54 other data combinations of three selected variables subjected to different data transformations. In Experiment 1, the best discrimination was reached using ten cube-transformed attributes: ~85% overall accuracy using six pieces of size information, three metoc variables, and one geo-location parameter. In Experiment 2, two combinations of three variables tied as the most effective: ~81% of overall accuracy using area (log transformed), length-to-width ratio (log- or cube-transformed), and number of feature parts (non-transformed). After verifying the classification accuracy of 114 algorithms by comparing with expert interpretations, we concluded that applying different data transformations and accounting for metoc and geo-location attributes optimizes the accuracies of binary classifiers (oil spill vs. look-alike slicks) using the simple LDA technique.<\/jats:p>","DOI":"10.3390\/rs13173466","type":"journal-article","created":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T23:05:12Z","timestamp":1630623912000},"page":"3466","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5282-9812","authenticated-orcid":false,"given":"Gustavo de Ara\u00fajo","family":"Carvalho","sequence":"first","affiliation":[{"name":"Laborat\u00f3rio de Sensoriamento Remoto por Radar Aplicado \u00e0 Ind\u00fastria do Petr\u00f3leo (LabSAR), Laborat\u00f3rio de M\u00e9todos Computacionais em Engenharia (LAMCE), Programa de Engenharia Civil (PEC), Instituto Alberto Luiz Coimbra de P\u00f3s-Gradua\u00e7\u00e3o e Pesquisa de Engenharia (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro 21941-859, RJ, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7961-6590","authenticated-orcid":false,"given":"Peter J.","family":"Minnett","sequence":"additional","affiliation":[{"name":"Department of Ocean Sciences (OCE), Rosenstiel School of Marine and Atmospheric Science (RSMAS), University of Miami (UM), Miami, FL 33149, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nelson F. F.","family":"Ebecken","sequence":"additional","affiliation":[{"name":"N\u00facleo de Transfer\u00eancia de Tecnologia (NTT), Programa de Engenharia Civil (PEC), Instituto Alberto Luiz Coimbra de P\u00f3s-Gradua\u00e7\u00e3o e Pesquisa de Engenharia (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro 21941-901, RJ, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luiz","family":"Landau","sequence":"additional","affiliation":[{"name":"Laborat\u00f3rio de Sensoriamento Remoto por Radar Aplicado \u00e0 Ind\u00fastria do Petr\u00f3leo (LabSAR), Laborat\u00f3rio de M\u00e9todos Computacionais em Engenharia (LAMCE), Programa de Engenharia Civil (PEC), Instituto Alberto Luiz Coimbra de P\u00f3s-Gradua\u00e7\u00e3o e Pesquisa de Engenharia (COPPE), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro 21941-859, RJ, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,1]]},"reference":[{"key":"ref_1","unstructured":"RCC (National Research Council Committee) (1985). 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