Estimation of Soil Moisture from Airborne Hyperspectral Imagery with Support Vector Regression
Détails
ID Serval
serval:BIB_7D45B638797D
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Sous-type
Abstract (résumé de présentation): article court qui reprend les éléments essentiels présentés à l'occasion d'une conférence scientifique dans un poster ou lors d'une intervention orale.
Collection
Publications
Institution
Titre
Estimation of Soil Moisture from Airborne Hyperspectral Imagery with Support Vector Regression
Titre de la conférence
WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Adresse
Gainesville, Florida, USA, June 25-28, 2013
Statut éditorial
Publié
Date de publication
2013
Langue
anglais
Résumé
In this paper, we propose to estimate soil moisture in
bare soils directly from hyperspectral imagery using
support vector regression (nu-SVR). nu-SVR is a
supervised non-parametric learning technique, e.g. making
no assumption on the underlying data distribution, which
shows good generalization properties even when only a
limited number of training samples is available (which is
often the case in soil moisture estimation). Estimation
in six tilled bare soil fields shows the potential of
using non-linear nu-SVR for the prediction of gravimetric
soil moisture. Dependence to the origin of training
samples, as well as their number, is thoroughly
considered.
bare soils directly from hyperspectral imagery using
support vector regression (nu-SVR). nu-SVR is a
supervised non-parametric learning technique, e.g. making
no assumption on the underlying data distribution, which
shows good generalization properties even when only a
limited number of training samples is available (which is
often the case in soil moisture estimation). Estimation
in six tilled bare soil fields shows the potential of
using non-linear nu-SVR for the prediction of gravimetric
soil moisture. Dependence to the origin of training
samples, as well as their number, is thoroughly
considered.
Mots-clé
LTS5, Soil moisture, Hyperspectral, Support Vector, Regression, non linear, Bare soils
Création de la notice
06/01/2014 21:46
Dernière modification de la notice
20/08/2019 15:38
Données d'usage
