Using three-dimensional spectral/spatial Gabor filters for hyperspectral region classification - art. no. 69660E

A 3-D spectral/spatial DFT represents an image region using a dense sampling in the frequency domain. An alternative approach is to represent a 3-D DFT by its projection onto a set of functions that capture specific orientation, scale, and spectral attributes of the image data. For this purpose, we have developed a new model for spectral/spatial information in images based on three-dimensional Gabor filters. This model achieves optimal joint localization in space and frequency and provides an efficient means of sampling a three-dimensional frequency domain representation of HSI data. Since 3-D Gabor filters allow for a large number of spectral/spatial quantities to be used to represent an image region, the performance and efficiency of algorithms that use this representation can be improved if methods are available to reduce the dimensionality of the model. Thus, we have derived methods for selecting filters that emphasize the most significant spectral/spatial differences between the various classes in a scene. We demonstrate the utility of the new model for region classification in AVIRIS data.