Evaluating effects of spectral training data distribution on continuous field mapping performance

Détails

ID Serval
serval:BIB_C5C4ABB22827
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Evaluating effects of spectral training data distribution on continuous field mapping performance
Périodique
ISPRS Journal of Photogrammetry and Remote Sensing
Auteur(s)
Mathys L., Guisan A., Kellenberger T.W., Zimmermann N.E.
ISSN
0924-2716
Statut éditorial
Publié
Date de publication
2009
Peer-reviewed
Oui
Volume
64
Numéro
6
Pages
665-673
Langue
anglais
Résumé
Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance.
We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models.
Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
Mots-clé
Geography, Physical, Geosciences, Multidisciplinary, Remote Sensing, Imaging Science & Photographic Technology
Web of science
Création de la notice
17/11/2009 9:36
Dernière modification de la notice
03/03/2018 21:16
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