Using multiband correlation models for the invariant recognition of 3-D hyperspectral textures

We develop a method for the recognition of three-dimensional (3-D) textures in hyperspectral images. Textures are modeled using subspaces of multiband correlation functions that represent texture variability over a range of solar angles and atmospheric conditions. These subspace models are used to enable texture recognition that is invariant to these environmental variables. The multiband correlation model captures within and between spectral band spatial characteristics. We present a method that can be used to optimize the selection of the multiband correlation functions for a given texture discrimination problem. We demonstrate the effectiveness of the approach using texture recognition experiments that consider 2016 texture samples from 168 hyperspectral images that were synthesized for a 3-D scene over a range of conditions. The results show that 3-D textures can be accurately recognized over a wide range of conditions using a small number of multiband correlation functions.