Centre dedocumentation
scientifique

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)

Public [article]  inRemote sensing of environment > vol 91 n° 2 (30/05/2004) . - pp 212 - 224 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 descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] forêt [Termes descripteurs IGN] houppier [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] ondelette [Termes descripteurs IGN] performance [Termes descripteurs IGN] réflectance de surface [Termes descripteurs IGN] transformation en ondelettes Résumé : (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 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26770 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
110-04101	RAB	Revue	Centre de documentation	En réserve 3L	Disponible