Combining Texture and Hyperspectral Information for the Classification of Tree Species in Australian Savanna Woodlands

This paper outlines research undertaken to assess the ability of textural information, from image filters, to be used alongside hyperspectral data for the classification of broad forest types. The study made use of 2.6 m hyperspectral HyMap data acquired over the Injune study area, Queensland, Australia, in September 2000. The HyMap data provided spectral data from the blue to shortwave infrared in 126 wavelengths, all of which were used for classification. A measure of texture was achieved using a set of 48 image filters including Laplacian of Guassian and Gaussian smoothing, first and second order derivatives at different scale and where appropriate different rotations. Analysis took place using an air photo interpretation to provide regions of interest for areas dominated by Angophora, Callitris, and Eucalyptus, additionally areas of non-forest were also included. Classification of the resulting dataset was performed using Multiple Stepwise Discriminant Analysis where an accuracy of 60% was achieved using the combined reflectance and texture data compared to accuracies of 55 and 43% using only the reflectance and textural datasets, respectively.