Wavelet based texture modeling for the classification of very high resolution maritime pine forest images
Langue
en
Communication dans un congrès
Ce document a été publié dans
IGARSS 2014, International Geoscience and Remote Sensing Symposium, 2014-07-13, Québec. 2014
IEEE
Résumé en anglais
This study evaluates the potential of wavelet-based texture modeling for the classification of stand age in a managed maritime pine forest using very high resolution satellite data. A cross-validation approach based on ...Lire la suite >
This study evaluates the potential of wavelet-based texture modeling for the classification of stand age in a managed maritime pine forest using very high resolution satellite data. A cross-validation approach based on stand age reference data shows that multivariate modeling of the spatial dependence of wavelet coefficients outperforms the use of features derived from co-occurrence matrices. Simultaneously adding features representing the color dependence and leveling the dominant orientation in anisotropic forest stands enhances the classification performances. These results obtained from panchromatic and multispectral PLEIADES data confirm the ability of such wavelet-based multivariate models to efficiently capture the textural properties of very high resolution forest data and opens up perspectives for their use in the mapping of mono-specific forest structure variables.< Réduire
Mots clés en anglais
forest structure
texture analysis
classification
very high resolution
wavelet
Origine
Importé de halUnités de recherche