Hyperspectral Region Classification Using a Three-Dimensional Gabor Filterbank

Abstract

A 3-D spectral/spatial discrete Fourier transform can be used to represent a hyperspectral image region using a dense sampling in the frequency domain. In many cases, a more compact frequency-domain representation that preserves the 3-D structure of the data can be exploited. For this purpose, we have developed a new model for spectral/spatial information based on 3-D Gabor filters. These filters capture specific orientation, scale, and wavelength-dependent properties of hyperspectral image data and provide an efficient means of sampling a 3-D frequency-domain representation. Since 3-D Gabor filters allow for a large number of spectral/spatial features to be used to represent an image region, the performance and efficiency of algorithms that use this representation can be further improved if methods are available to reduce the size of the model. Thus, we have derived methods for selecting features that emphasize the most significant spectral/spatial differences between the various classes in a scene. We demonstrate the performance of the 3-D Gabor features for the classification of regions in Airborne Visible/Infrared Imaging Spectrometer hyperspectral data. The new features are compared against pure spectral features and multiband generalizations of gray-level co-occurrence matrix features.