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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]

A data-mining approach to associating MISR smoke plume heights with MODIS fire measurements / 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 138 - 148 Titre : A data-mining approach to associating MISR smoke plume heights with MODIS fire measurements Type de document : Article/Communication Auteurs : D. Mazzoni, Auteur ; J.A. Logan, Auteur ; D. Diner, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 138 - 148 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] aérosol [Termes IGN] Amérique du nord [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] exploration de données [Termes IGN] fumée [Termes IGN] image Terra-MISR [Termes IGN] image Terra-MODIS [Termes IGN] incendie [Termes IGN] nuage Résumé : (Auteur) Satellites provide unique perspectives on aerosol global and regional spatial and temporal distributions, and offer compelling evidence that visibility and air quality are affected by particulate matter transported over long distances. The heights at which emissions are injected into the atmosphere are major factors governing downwind dispersal. In order to better understand the environmental factors determining injection heights of smoke plumes from wildfires, we have developed a prototype system for automatically searching through several years of MISR and MODIS data to locate fires and the associated smoke plumes and to retrieve injection heights and other relevant measurements from them. We are refining this system and assembling a statistical database, aimed at understanding how injection height relates to the fire severity and local weather conditions. In this paper we focus on our working proof-of-concept system that demonstrates how machine-learning and data mining methods aid in processing of massive volumes of satellite data. Automated algorithms for distinguishing smoke from clouds and other aerosols, identifying plumes, and extracting height data are described. Preliminary results are presented from application to MISR and MODIS data collected over North America during the summer of 2004. Copyright Elsevier Numéro de notice : A2007-053 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.08.014 En ligne : https://doi.org/10.1016/j.rse.2006.08.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28418 [article]