Hyperspectral image classification using three-dimensional geometric moments

Abstract

Exploitation of both spectral and spatial information in hyperspectral imagery is important for effective classification. Considering the cubical arrangement of data, the three-dimensional (3D) techniques could be effectively used to model hypespectral features. In this study, the 3D geometric moments are used to extract the rotation, scale, and translation invariant features. Unlike 2D moments, the 3D moments characterise the joint spectral-spatial properties. A classification method is proposed that uses the features derived from 3D geometric moments without vectorising or changing the original structure of the raw hyperspectral image. Unlike many other methods, the new method does not need a separate step for spectral feature extraction or dimensionality reduction. The moments are computed on the raw image that generate a comprehensive and smaller feature set. The experimental results from five benchmark airborne hyperspectral images demonstrate that the 3D moment based method yields good classification results better or comparable to several state-of-the-art methods.