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Auteur R. Pu |
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Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping / R. Pu in Remote sensing of environment, vol 91 n° 2 (30/05/2004)
[article]
Titre : Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping Type de document : Article/Communication Auteurs : R. Pu, Auteur ; P. Gong, Auteur Année de publication : 2004 Article en page(s) : pp 212 - 224 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse en composantes principales
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] carte de la végétation
[Termes IGN] extraction automatique
[Termes IGN] forêt
[Termes IGN] houppier
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] Leaf Area Index
[Termes IGN] ondelette
[Termes IGN] performance
[Termes IGN] réflectance de surface
[Termes IGN] transformation en ondelettesRésumé : (Auteur) A comparison of the performance of three feature extraction methods was made for mapping forest crown closure (CC) and leaf area index (LAI) with EO-1 Hyperion data. The methods are band selection (SB), principal component analysis (PCA) and wavelet transform (WT). Hyperion data were acquired on October 9, 200 1. A total of 38 field measurements of CC and LAI were collected on August 10 - 11, 2001, at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) conducting atmospheric correction with High Accuracy Atmospheric Correction for Hyperspectral Data (HATCH) to retrieve surface reflectance, (2) extracting features with the three methods: SB, PCA and WT, (3) establishing multivariate regression prediction models, (4) predicting and mapping pixel-based CC and LAI values, and (5) validating the CC and LAI mapped results with photo-interpreted CC and LAI values. The experimental results indicate that the energy features extracted by the WT method are the most effective for mapping forest CC and LAI (mapped accuracy (MA) for CC = 84.90%, LAI MA= 75.39%), followed by the PCA method (CC MA= 77.42%, LAI MA= 52.36%). The SB method performed the worst (CC MA= 57.77%, LAI MA= 50.87%). Numéro de notice : A2004-243 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.03.006 En ligne : https://doi.org/10.1016/j.rse.2004.03.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26770
in Remote sensing of environment > vol 91 n° 2 (30/05/2004) . - pp 212 - 224[article]