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A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 15 p. Titre : A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing Type de document : Article/Communication Auteurs : Ran Pelta, Auteur ; Nimrod Carmon, Auteur ; Eyal Ben-Dor, Auteur Année de publication : 2019 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] étalonnage de modèle [Termes IGN] hydrocarbure [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] image proche infrarouge [Termes IGN] Israël [Termes IGN] Kappa de Cohen [Termes IGN] pétrole [Termes IGN] photo-interprétation [Termes IGN] pollution des sols [Termes IGN] réflectance du sol [Termes IGN] spectroscopie Résumé : (auteur) One of the most ubiquitous and detrimental types of environmental contamination in the world is crude oil pollution. When released into either the aquatic or terrestrial environments, this pollution can negatively impact flora and fauna, as well as human health. Hence, a rapid and affordable spatial assessment of the pollution is favored to limit the spill’s effects. Using airborne hyperspectral remote sensing (HRS) for crude oil detection in terrestrial areas has been investigated in previous studies, which mainly relied on heavily oiled artificial samples. These studies and others based their methodologies on the premise that the spectral features of petroleum hydrocarbon (PHC) are clearly observable, which might not be true in all cases. In this study, we aimed at assessing the true potential of using HRS for terrestrial oil spill mapping in a real disaster site in southern Israel, where laboratory and controlled conditions do not apply. Using the AISA SPECIM Fenix1 K sensor, we collected airborne image of the study site and analyzed the data with advanced data mining techniques. Various challenges and limitations arose from the airborne HRS image being taken two and a half years after the crude oil had been released into the environment and exposed to the surface. Here, no spectral features of PHC were detectable in the spectrum, preventing the use of PHC indices and spectral methods developed by others. Nevertheless, by using standardization techniques, vicarious band selection, dimension reduction, multivariate calibration, and supervised machine-learning, we were able to successfully distinguish between contaminated pixels from non-contaminated ones. Classification accuracy metrics of overall accuracy, sensitivity, specificity, and Kappa yielded good results of 0.95, 0.95, 0.95 and 0.9, respectively, for cross-validation, and 0.93, 0.91, 0.94 and 0.85, for the validation dataset. Classified image and test scenes also showed strong agreement with an orthophoto image taken several days after the disaster, when the pollution was clearly visible. Thus, we conclude that HRS technology can detect PHC traces in an oil spill site, even under the most challenging conditions. Numéro de notice : A2019-475 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.101901 Date de publication en ligne : 22/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.101901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93636 [article]