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An operational MISR pixel classifier using support vector machines / D. Mazzoni in Remote sensing of environment, vol 107 n° 1-2 (15 March 2007)  

Public [article]  inRemote sensing of environment > vol 107 n° 1-2 (15 March 2007) . - pp 149 - 158 Titre : An operational MISR pixel classifier using support vector machines Type de document : Article/Communication Auteurs : D. Mazzoni, Auteur ; M.J. Garay, Auteur ; R. Davies, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 149 - 158 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage automatique [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Terra-MISR Résumé : (Auteur) The Multi-angle Imaging SpectroRadiometer (MISR) data products now include a scene classification for each 1.1-km pixel that was developed using Support Vector Machines (SVMs), a cutting-edge machine learning technique for supervised classification. Using a combination of spectral, angular, and texture features, each pixel is classified as land, water, cloud, aerosol, or snow/ice, with the aerosol class further divided into smoke, dust, and other aerosols. The classifier was trained by MISR scientists who labeled hundreds of scenes using a custom interactive tool that showed them the results of the training in real time, making the process significantly faster. Preliminary validation shows that the accuracy of the classifier is approximately 81% globally at the 1.1-km pixel level. Applications of this classifier include global studies of cloud and aerosol distribution, as well as data mining applications such as searching for smoke plumes. This is one of the largest and most ambitious operational uses of machine learning techniques for a remote-sensing instrument, and the success of this system will hopefully lead to further use of this approach. Copyright Elsevier Numéro de notice : A2007-054 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.06.021 En ligne : https://doi.org/10.1016/j.rse.2006.06.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28419 [article]