Extended Morphological Profile-based Gabor Wavelets for Hyperspectral Image Classification

Abstract

Hyperspectral image acquired by a hyperspectral sensor contains hundred of narrow contiguous spectral bands, since the spatial distribution of surface materials generally exhibits high regularity and local continuity, spatial texture information should be introduced to improve the classification accuracy of hyperspectral image. The extended morphological profiles (EMP) have been created from the raw hyperspectral image, which has proven to be effective and robust of reflecting the spatial structural features of hyperspectral data. In the meanwhile, because the three-dimensional (3D) Gabor wavelets have been introduced to exploit the joint spectral-spatial features of hyperspectral image. In this paper, for the purpose of combining the advantages of the EMP operator and Gabor wavelet transform together, an extended morphological profile-based Gabor wavelets, which is named as EMP-Gabor, has been proposed for hyperspectral image classification. Definitely, to compute principal components of the hyperspectral image, the most significant principal components are used as base images for an extended morphological profile, and the EMP features can be thus obtained. Secondly, 3D Gabor wavelets with particular orientations are directly convolved with the EMP feature cube. Finally, support vector machine (SVM) classifier is utilized to carry out the classification task. Experimental results on two real hyperspectral data sets have demonstrated the effectiveness of the proposed EMP-Gabor framework for hyperspectral image classification over several state-of-the-art methods.