Wavelet-Based Texture Features for the Classification of Age Classes in a Maritime Pine Forest

Abstract

This letter evaluates the potential of wavelet-based texture modeling for the classification of stand age in a managed maritime pine forest using very high resolution panchromatic and multispectral PLEIADES data. A cross-validation approach based on stand age reference data is used to compare classification performances obtained from different multivariate models (multivariate Gaussian, spherically invariant random vector (SIRV)-based models, and Gaussian copulas) and from co-occurrence matrices. Results show that the multivariate modeling of the spatial dependence of wavelet coefficients (particularly when using the Gaussian SIRV model) 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 confirm the ability of such wavelet-based multivariate models to efficiently capture the textural properties of very high resolution forest data and open up perspectives for their use in the mapping of monospecific forest structure variables.