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Processing Hyperion and ALI for forest classification / D.G. Goodenough in IEEE Transactions on geoscience and remote sensing, vol 41 n° 6 (June 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 6 (June 2003) . - pp 1321 - 1337 Titre : Processing Hyperion and ALI for forest classification Type de document : Article/Communication Auteurs : D.G. Goodenough, Auteur ; A. Dyk, Auteur ; K.O. Niemann, Auteur ; et al., Auteur Année de publication : 2003 Article en page(s) : pp 1321 - 1337 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] Canada [Termes IGN] classification dirigée [Termes IGN] correction d'image [Termes IGN] Etats-Unis [Termes IGN] forêt [Termes IGN] image EO1-ALI [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] image Landsat-ETM+ [Termes IGN] image multibande [Termes IGN] Pinophyta [Termes IGN] Pinus contorta [Termes IGN] Pseudotsuga menziesii [Termes IGN] Thuja plicata Résumé : (Auteur) Hyperion (a hyperspectral sensor) and the Advanced Land Imager (ALI) (a multispectral sensor) are carried on the National Aeronautics and Space Administration's Earth Observing 1 (EO-1) satellite. The Evaluation and Validation of EO-1 for substainable Development (EVEOSD) is our project supporting the EO-1 mission. With 10% of the world's forests and the second country by area in the world, Canada has a natural requirement for effective monitoring of its forests. Eight test sites have been selected for EVEOSD, with seven in Canada and one United States. Extensive fieldwork has been conducted at four of these sites. A comparison is made of forest classification from Hyperion, ALI, and the Enhanced Thematic Mapper (ETM+) of Landsat-7 for the Greater Victoria Watershed. The data have been radiometrically corrected and orthorectified. Feature selection and statistical transforms are used to reduce the Hyperion feature space from 198 channels to 11 features. Classes chosen for discrimination included Douglasfir, hemlock, western, redcedar, lodgepole pine, and red alder. Overall classification accuracies obtained for each sensor were Hyperion 90.0 %, ALI 84.8% and ETM+ 75.0%. Hyperspectral remote sensing provides significant advantages and greater accuracies over ETM+ for forest discrimination. The EO-1 sensors, Hyperion and ALI, provide data with excellent discrimination for Pacific Northwest in comparison to Landsat-7 ETM+. Numéro de notice : A2003-216 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.813214 En ligne : https://doi.org/10.1109/TGRS.2003.813214 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22512 [article]

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