{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:00:59Z","timestamp":1784131259124,"version":"3.55.0"},"reference-count":94,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Brazilian National Postdoctoral Program (Programa Nacional de P\u00f3s Doutorado: PNPD) of the Coordination for the Improvement of Higher Education Personnel (Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior: CAPES)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea-surface petroleum pollution is observed as \u201coil slicks\u201d (i.e., \u201coil spills\u201d or \u201coil seeps\u201d) and can be confused with \u201clook-alike slicks\u201d (i.e., environmental phenomena, such as low-wind speed, upwelling conditions, chlorophyll, etc.) in synthetic aperture radar (SAR) measurements, the most proficient satellite sensor to detect mineral oil on the sea surface. Even though machine learning (ML) has become widely used to classify remotely-sensed petroleum signatures, few papers have been published comparing various ML methods to distinguish spills from look-alikes. Our research fills this gap by comparing and evaluating six traditional techniques: simple (naive Bayes (NB), K-nearest neighbor (KNN), decision trees (DT)) and advanced (random forest (RF), support vector machine (SVM), artificial neural network (ANN)) applied to different combinations of satellite-retrieved attributes. 36 ML algorithms were used to discriminate \u201cocean-slick signatures\u201d (spills versus look-alikes) with ten-times repeated random subsampling cross validation (70-30 train-test partition). Our results found that the best algorithm (ANN: 90%) was &gt;20% more effective than the least accurate one (DT: ~68%). Our empirical ML observations contribute to both scientific ocean remote-sensing research and to oil and gas industry activities, in that: (i) most techniques were superior when morphological information and Meteorological and Oceanographic (MetOc) parameters were included together, and less accurate when these variables were used separately; (ii) the algorithms with the better performance used more variables (without feature selection), while lower accuracy algorithms were those that used fewer variables (with feature selection); (iii) we created algorithms more effective than those of benchmark-past studies that used linear discriminant analysis (LDA: ~85%) on the same dataset; and (iv) accurate algorithms can assist in finding new offshore fossil fuel discoveries (i.e., misclassification reduction).<\/jats:p>","DOI":"10.3390\/rs14133027","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T22:50:23Z","timestamp":1656283823000},"page":"3027","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Machine-Learning Classification of SAR Remotely-Sensed Sea-Surface Petroleum Signatures\u2014Part 1: Training and Testing Cross Validation"],"prefix":"10.3390","volume":"14","author":[{"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":[[2022,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8364","DOI":"10.1002\/2015JC011062","article-title":"Natural and Unnatural Oil Slicks in the Gulf of Mexico","volume":"120","author":"MacDonald","year":"2015","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2012.03.024","article-title":"Review\u2014State of the Art Satellite and Airborne Marine Oil Spill Remote Sensing: Application to the BP Deepwater Horizon Oil Spill","volume":"124","author":"Leifer","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ward, C. (2017). Oil and Gas Seeps in the Gulf of Mexico. Habitats and Biota of the Gulf of Mexico: Before the Deepwater Horizon Oil Spill, Springer. Chapter 5.","DOI":"10.1007\/978-1-4939-3447-8"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6251","DOI":"10.1029\/JC094iC05p06251","article-title":"The Damping of Ocean Waves by Surface Films: A New Look at an Old Problem","volume":"94","author":"Alpers","year":"1989","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_5","unstructured":"API (American Petroleum Institute) (2013). Remote Sensing in Support of Oil Spill Response: Planning Guidance, Technical Report No. 1144; American Petroleum Institute. Available online: https:\/\/www.oilspillprevention.org\/-\/media\/Oil-Spill-Prevention\/spillprevention\/r-and-d\/oil-sensing-and-tracking\/1144-e1-final.pdf."},{"key":"ref_6","first-page":"563","article-title":"Analysis of Environmental and Economic Damages from British Petroleum\u2019s Deepwater Horizon Oil Spill","volume":"74","author":"Smith","year":"2011","journal-title":"Albany Law Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s13280-010-0085-5","article-title":"The Threats from Oil Spills: Now, Then, and in the Future","volume":"39","author":"Jernelov","year":"2010","journal-title":"AMBIO"},{"key":"ref_8","unstructured":"Brown, C.E., and Fingas, M. New Space-Borne Sensors for Oil Spill Response. Proceedings of the International Oil Spill Conference."},{"key":"ref_9","unstructured":"Brown, C.E., and Fingas, M. (2009, January 12\u201314). The Latest Developments in Remote Sensing Technology for Oil Spill Detection. Proceedings of the Interspill Conference and Exhibition, Marseille, France."},{"key":"ref_10","unstructured":"Jackson, C.R., and Apel, J.R. (2004). Synthetic Aperture Radar Marine User\u2019s Manual, NOAA\/NESDIS, Office of Research and Applications. Available online: https:\/\/www.sarusersmanual."},{"key":"ref_11","unstructured":"Espedal, H.A. (1998). Detection of Oil Spill and Natural Film in the Marine Environment by Spaceborne Synthetic Aperture Radar. [Ph.D. Thesis, Department of Physics, University of Bergen and Nansen Environmental and Remote Sensing Center (NERSC)]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1023\/A:1007452223027","article-title":"Machine Learning for the Detection of Oil Spills in Satellite Radar Images","volume":"30","author":"Kubat","year":"1998","journal-title":"Mach. Learn."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2017.09.002","article-title":"Oil Spill Detection by Imaging Rradars: Challenges and Pitfalls","volume":"201","author":"Alpers","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_14","unstructured":"Genovez, P.C. (2010). Segmenta\u00e7\u00e3o e Classifica\u00e7\u00e3o de Imagens SAR Aplicadas \u00e0 Detec\u00e7\u00e3o de Alvos Escuros em \u00c1reas Oce\u00e2nicas de Explora\u00e7\u00e3o e Produ\u00e7\u00e3o de Petr\u00f3leo. [Ph.D. Dissertation, COPPE]. Available online: http:\/\/www.coc.ufrj.br\/index.php\/teses-de-doutorado\/154-2010\/1239-patricia-carneiro-genovez."},{"key":"ref_15","unstructured":"Bentz, C.M. (2006). Reconhecimento Autom\u00e1tico de Eventos Ambientais Costeiros e Oce\u00e2nicos em Imagens de Radares Orbitais. [Ph.D. Thesis, COPPE]. Available online: http:\/\/www.coc.ufrj.br\/index.php?option=com_content&view=article&id=1048:cristina-maria-bentz."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S1353-2561(98)00023-1","article-title":"Review of Oil Spill Remote Sensing","volume":"4","author":"Fingas","year":"1997","journal-title":"Spill Sci. Technol. Bull."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.marpolbul.2014.03.059","article-title":"Review of Oil Spill Remote Sensing","volume":"15","author":"Fingas","year":"2014","journal-title":"Mar. Pollut. Bull."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fingas, M., and Brown, C.E. (2018). A Review of Oil Spill Remote Sensing. Sensors, 18.","DOI":"10.3390\/s18010091"},{"key":"ref_19","unstructured":"Carvalho, G.A. (2015). Multivariate Data Analysis of Satellite-Derived Measurements to Distinguish Natural from Man-Made Oil Slicks on the Sea Surface of Campeche Bay (Mexico). [Ph.D. Thesis, COPPE]. Available online: http:\/\/www.coc.ufrj.br\/index.php?option=com_content&view=article&id=4618:gustavo-de-araujo-carvalho."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1145\/219717.219768","article-title":"Applications of Machine Learning and Rule Induction","volume":"38","author":"Langley","year":"1995","journal-title":"Commun. ACM"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine Learning in Geosciences and Remote Sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"27842817","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Al-Ruzouq, R., Gibril, M.B.A., Shanableh, A., Kais, A., Hamed, O., Al-Mansoori, S., and Khalil, M.A. (2020). Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12203338"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A Survey of Image Classification Methods and Techniques for Improving Classification Performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"042609","DOI":"10.1117\/1.JRS.11.042609","article-title":"Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools, and Challenges for the Community","volume":"11","author":"Ball","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"McLachlan, G. (1992). Discriminant Analysis and Statistical Pattern Recognition, A Whiley-Interescience Publication, John Wiley & Sons, Inc.","DOI":"10.1002\/0471725293"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Carvalho, G.A., Minnett, P.J., Miranda, F.P., Landau, L., and Paes, E.T. (2017). Exploratory Data Analysis of Synthetic Aperture Radar (SAR) Measurements to Distinguish the Sea Surface Expressions of Naturally-Occurring Oil Seeps from Human-Related Oil Spills in Campeche Bay (Gulf of Mexico). ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6120379"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Carvalho, G.A., Minnett, P.J., Paes, E.T., Miranda, F.P., and Landau, L. (2018). Refined Analysis of RADARSAT-2 Measurements to Discriminate Two Petrogenic Oil-Slick Categories: Seeps versus Spills. J. Mar. Sci. Eng., 6.","DOI":"10.3390\/jmse6040153"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Carvalho, G.A., Minnett, P.J., Paes, E.T., Miranda, F.P., and Landau, L. (2019). Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients in Campeche Bay (Gulf of Mexico). Remote Sens., 11.","DOI":"10.3390\/rs11141652"},{"key":"ref_30","first-page":"307","article-title":"The Use of a RADARSAT-Derived Long-Term Dataset to Investigate the Sea Surface Expressions of Human-Related Oil Spills and Naturally-Occurring Oil Seeps in Campeche Bay, Gulf of Mexico","volume":"42","author":"Carvalho","year":"2016","journal-title":"Can. J. Remote Sens. Spec. Issue Long-Term Satell. Data Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Carvalho, G.A., Minnett, P.J., Ebecken, N.F.F., and Landau, L. (2020). Classification of Oil Slicks and Look-Alike Slicks: A Linear Discriminant Analysis of Microwave, Infrared, and Optical Satellite Measurements. Remote Sens., 12.","DOI":"10.3390\/rs12132078"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Carvalho, G.A., Minnett, P.J., Ebecken, N.F.F., and Landau, L. (2021). Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data. Remote Sens., 13.","DOI":"10.3390\/rs13173466"},{"key":"ref_33","unstructured":"Kevin, P.M. (2012). Machine Learning: A Probabilistic Perspective. MIT Press."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lampropoulos, A.S., and Tsihrintzis, G.A. (2015). The Learning Problem. Graduate Texts in Mathematics, Humana Press.","DOI":"10.1007\/978-3-319-19135-5_3"},{"key":"ref_35","unstructured":"Stephen, M. (2009). Machine Learning an Algorithmic Perspective, CRC Press. [2nd ed.]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2013.10.012","article-title":"A Comparative Study of Different Classification Techniques for Marine Oil Spill Identification Using RADARSAT-1 Imagery","volume":"141","author":"Xu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Garcia-Pineda, O., Holmes, J., Rissing, M., Jones, R., Wobus, C., Svejkovsky, J., and Hess, M. (2017). Detection of Oil near Shorelines During the Deepwater Horizon Oil Spill Using Synthetic Aperture Radar (SAR). Remote Sens., 9.","DOI":"10.3390\/rs9060567"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.marpol.2020.103879","article-title":"Oil Spill in South Atlantic (Brazil): Environmental and Governmental Disaster","volume":"115","author":"Soares","year":"2020","journal-title":"Mar. Policy"},{"key":"ref_39","unstructured":"Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in Data Management Systems Morgan Kaufmann Publishers. [3rd ed.]."},{"key":"ref_40","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2000). An Introduction to Statistical Learning, Springer."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Carvalho, G.A., Minnett, P.J., Ebecken, N.F.F., and Landau, L. (2022). Machine-Learning Classification of SAR Remotely-Sensed Sea-Surface Petroleum Signatures\u2014Part 2: Validation Phase Using New, Unseen Data from Different Regions. in preparation.","DOI":"10.3390\/rs14133027"},{"key":"ref_42","first-page":"2349","article-title":"Orange: Data Mining Toolbox in Python","volume":"14","author":"Demsar","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","first-page":"55","article-title":"Orange: Data Mining Fruitful and Fun\u2014A Historical Perspective","volume":"37","author":"Demsar","year":"2013","journal-title":"Informatica"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jovic, A., Brkic, K., and Bogunovic, N. (2015, January 25\u201329). A Review of Feature Selection Methods with Applications. Proceedings of the 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.1109\/MIPRO.2015.7160458"},{"key":"ref_45","first-page":"1205","article-title":"Efficient Feature Selection via Analysis of Relevance and Redundancy","volume":"5","author":"Yu","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Aggarwal, C., and Reddy, C. (2013). Feature Selection for Clustering: A Review. Data Clustering: Algorithms and Applications, CRC Press.","DOI":"10.1201\/b15410"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shah, F.P., and Patel, V. (2016, January 23\u201325). A Review on Feature Selection and Feature Extraction for Text Classification. Proceedings of the International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), IEEE, Chennai, India.","DOI":"10.1109\/WiSPNET.2016.7566545"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ipm.2004.08.006","article-title":"Information Gain and Divergence-Based Feature Selection for Machine Learning-Based Text Categorization","volume":"42","author":"Lee","year":"2006","journal-title":"Inf. Processing Manag."},{"key":"ref_49","first-page":"18","article-title":"Feature Selection Based on Information Gain","volume":"2","author":"Azhagusundari","year":"2013","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_50","unstructured":"Harris, E. (2002). Information Gain Versus Gain Ratio: A Study of Split Method Biases. Annals of Mathematics and Artificial Intelligence (ISAIM), Computer Science Department William & Mary."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"201","DOI":"10.21917\/ijsc.2011.0031","article-title":"Gain Ratio Based Feature Selection Method for Privacy Preservation","volume":"1","author":"Priyadarsini","year":"2011","journal-title":"ICTACT J. Soft Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2006.04.001","article-title":"A Novel Feature Selection Algorithm for Text Categorization","volume":"33","author":"Shang","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","article-title":"Model Selection and Estimation in Regression with Grouped Variables","volume":"68","author":"Yuan","year":"2006","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3085","DOI":"10.1016\/j.eswa.2010.08.100","article-title":"Using Chi-Square Statistics to Measure Similarities for Text Categorization","volume":"38","author":"Chen","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","article-title":"Relief-Based Feature Selection: Introduction and Review","volume":"85","author":"Urbanowicz","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Senliol, B., Gulgezen, G., Yu, L., and Cataltepe, Z. (2008, January 27\u201329). Fast Correlation Based Filter (FCBF) with a Different Search Strategy. Proceedings of the 23rd International Symposium on Computer and Information Sciences, IEEE, Istanbul, Turkey.","DOI":"10.1109\/ISCIS.2008.4717949"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1093\/biomet\/76.3.503","article-title":"A Comparative Study of Ordinary Cross-Validation, v-Fold Cross-Validation and the Repeated Learning-Testing Methods","volume":"76","author":"Burman","year":"1989","journal-title":"Biometrika"},{"key":"ref_58","unstructured":"Gholamy, A., Kreinovich, V., and Kosheleva, O. (2018). Why 70\/30 or 80\/20 Relation Between Training and Testing Sets: A Pedagogical Explanation, Departmental Technical Reports (CS)."},{"key":"ref_59","unstructured":"EMSA (European Maritime Safety Agency) (2022, May 19). Near Real Time European Satellite Based Oil Spill Monitoring and Vessel Detection Service, 2nd Generation. Available online: https:\/\/portal.emsa.europa.eu\/web\/csn."},{"key":"ref_60","unstructured":"Moutinho, A.M. (2011). Otimiza\u00e7\u00e3o de Sistemas de Detec\u00e7\u00e3o de Padr\u00f5es em Imagens. [Ph.D. Thesis, COPPE]. Available online: http:\/\/www.coc.ufrj.br\/index.php\/teses-de-doutorado\/155-2011\/1258-adriano-martins-moutinho."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"258","DOI":"10.5589\/m04-014","article-title":"RADARSAT-2 SAR Modes Development and Utilization","volume":"30","author":"Fox","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.1109\/TGRS.2004.831685","article-title":"Evaluation of High-Resolution Ocean Surface Vector Winds Measured by QuikSCAT Scatterometer in Coastal Regions","volume":"42","author":"Tang","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"9179","DOI":"10.1029\/1999JC000065","article-title":"Overview of the NOAA\/NASA Pathfinder Algorithm for Sea-Surface Temperature and Associated Matchup Database","volume":"106","author":"Kilpatrick","year":"2001","journal-title":"J. Geophys. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2015.04.023","article-title":"A Decade of Sea-Surface Temperature from MODIS","volume":"165","author":"Kilpatrick","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_65","unstructured":"Hooker, S.B., and Firestone, E.R. (2002). SeaWiFS Postlaunch Calibration and Validation Analyses. NASA Tech. Memo, 2000-2206892, NASA Goddard Space Flight Center. Part 3."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/36.701076","article-title":"An Overview of MODIS Capabilities for Ocean Science Observations","volume":"36","author":"Esaias","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","first-page":"18550","article-title":"Water Mass Characteristics and Geostrophic Circulation in the South Brazil Bight: Summer of 91","volume":"100","author":"Campos","year":"1995","journal-title":"J. Geophys. Res."},{"key":"ref_68","unstructured":"Carvalho, G.A. (2002). Wind Influence on the Sea-Surface Temperature of the Cabo Frio Upwelling (23\u00b0S\/42\u00b0W\u2014RJ\/Brazil) During 2001, Through the Analysis of Satellite Measurements (Seawinds-QuikScat\/AVHRR-NOAA). [Bachelor\u2019s Thesis, UERJ]."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1590\/S1413-77392000000200008","article-title":"The Brazil Current off the Eastern Brazilian Coast","volume":"48","author":"Silveira","year":"2000","journal-title":"Rev. Bras. De Oceanogr."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Izadi, M., Sultan, M., Kadiri, R.E., Ghannadi, A., and Abdelmohsen, K. (2021). A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom. Remote Sens., 13.","DOI":"10.3390\/rs13193863"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_72","unstructured":"Zar, H.J. (2014). Biostatistical Analysis, Pearson New International Edition; Pearson. [5th ed.]."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1023\/A:1007413511361","article-title":"On the Optimality of the Simple Bayesian Classifier under Zero-One Loss","volume":"29","author":"Domingos","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_74","first-page":"25","article-title":"k-Nearest Neighbour Classifiers\u2014A Tutorial","volume":"54","author":"Cunningham","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_76","first-page":"1144","article-title":"Random Forest Classifiers: A Survey and Future Research Directions","volume":"36","author":"Kulkarni","year":"2013","journal-title":"Int. J. Adv. Comput."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1214\/088342306000000493","article-title":"Support Vector Machines with Applications","volume":"21","author":"Moguerza","year":"2006","journal-title":"Stat. Sci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/380995.380999","article-title":"Support Vector Machines: Hype or Hallelujah?","volume":"2","author":"Bennett","year":"2000","journal-title":"SIGKDD Explor."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Support Vector Machines for Classification. Efficient Learning Machines, Apress. Chapter 3.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial on Support Vector Machines for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0893-6080(03)00169-2","article-title":"Practical Selection of SVM Parameters and Noise Estimation for SVM Regression","volume":"17","author":"Cherkassky","year":"2004","journal-title":"Neural Netw."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support Vector Machines in Remote Sensing: A Review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_85","unstructured":"Haykin, S. (2008). Neural Networks and Learning Machines, Prentice Hall. [3rd ed.]."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3389\/fpubh.2017.00307","article-title":"Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice","volume":"5","author":"Trevethan","year":"2017","journal-title":"Front. Public Health"},{"key":"ref_87","first-page":"37","article-title":"Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation","volume":"2","author":"Powers","year":"2011","journal-title":"J. Mach. Learn. Technol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A Review of Assessing the Accuracy of Classification of Remote Sensed Data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., and Brunk, C. (1994, January 10\u201313). Reducing Misclassification Costs. Proceedings of the 11th International Conference on Machine Learning, New Brunswick, NJ, USA.","DOI":"10.1016\/B978-1-55860-335-6.50034-9"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1126\/science.3287615","article-title":"Measuring the Accuracy of Diagnostic Systems","volume":"240","author":"Swets","year":"1988","journal-title":"Science"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Lewis, D., and Gale, W. (1994, January 3\u20136). A Sequential Algorithm for Training Text Classifiers. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland.","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.rse.2008.09.005","article-title":"Benchmarking Classifiers to Optimally Integrate Terrain Analysis and Multispectral Remote Sensing in Automatic Rock Glacier Detection","volume":"113","author":"Brenning","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1021\/ac50011a041","article-title":"Classification of Petroleum Pollutants by Linear Discriminant Function Analysis of Infrared Spectral Patterns","volume":"49","author":"Mattson","year":"1977","journal-title":"Anal. Chem."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, L., and Clausi, D. (2017). Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004-2013) RADARSAT Data. Remote Sens., 9.","DOI":"10.3390\/rs9101041"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3027\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:39:23Z","timestamp":1760139563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3027"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,24]]},"references-count":94,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133027"],"URL":"https:\/\/doi.org\/10.3390\/rs14133027","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,24]]}}}