Spectral–Spatial Pixel Characterization Using Gabor Filters for Hyperspectral Image Classification

Abstract

This paper presents a spectral-spatial pixel characterization method for hyperspectral images. The characterization is based on textural features obtained using Gabor filters over a selected set of spectral bands. This scheme aims at improving land-use classification results decreasing significantly the number of spectral bands needed in order to reduce the dimensionality of the task thanks to an adequate description of the spatial characteristics of the image. This allows to require less data and avoid the curse of dimensionality. Very promising results are obtained which are similar or better than previous classification results provided by other spectral-spatial methods, but here also reducing the complexity using a reduced number of spectral bands.