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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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065-03061	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Semi-automated extraction of rivers from digital imagery / C.R. Dillabaugh in Geoinformatica, vol 6 n° 3 (September - November 2002)  

Public [article]  inGeoinformatica > vol 6 n° 3 (September - November 2002) . - pp 263 - 284 Titre : Semi-automated extraction of rivers from digital imagery Type de document : Article/Communication Auteurs : C.R. Dillabaugh, Auteur ; K.O. Niemann, Auteur ; D.E. Richardson, Auteur Année de publication : 2002 Article en page(s) : pp 263 - 284 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection de contours [Termes IGN] extraction semi-automatique [Termes IGN] image à haute résolution [Termes IGN] image numérique [Termes IGN] image panchromatique [Termes IGN] image SPOT [Termes IGN] réseau hydrographique [Termes IGN] rivière Résumé : (Auteur) The manual production of vector maps from digital imagery can be a time consuming and costly process. Developing tools to automate this task for specific features, such as roads, has become an important research topic. The purpose of this paper was to present a technique for the semi-automatic extraction of multiple pixel width river features appearing in high resolution satellite imagery. This was accomplished using a two stage, multi resolution procedure. Initial river extraction was performed on low resolution (SPOT multispectral 20 m) imagery. The results from this low resolution extraction were then refined on higher resolution (KFA 1000. panchromatic. 5m) imagery to produce a detailed outline of the channel banks. To perform low resolution extraction a cost surface was generated to represent the combined local evidence of the presence of a river feature. The local evidence of a river was evaluated based on the results of a number of simple operators. Then, with user specified start and end points for the network, rivers were extracted by performing a least cost path search across this surface using the A* algorithm. The low resolution results were transferred to the high resolution imagery as closed contours which provided an estimate of the channel banks. These contours were then fit to the channel banks using the dynamic contours (or snakes) technique. Numéro de notice : A2002-205 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1023/A:1019718019825 En ligne : https://doi.org/10.1023/A:1019718019825 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22120 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-02031	RAB	Revue	Centre de documentation	En réserve L003	Disponible