Moment-based feature extraction from high spatial resolution hyperspectral images

ABSTRACT

In this article, we propose a method for extracting spatio-spectral features from high spatial resolution hyperspectral (HS) images. The method is based on extracting two-dimensional moments from neighbourhoods of pixels. Three different types of moments are considered: geometric, complex Zernike and Legendre. Moments of a given type are extracted from a few principal components (PC) of HS data, and are stacked on the original HS data to form a joint spatio-spectral feature space. These features are classified using a support vector machine (SVM) classifier. The influence of the moments orders and the size of the neighbourhood window on the quality of the extracted features are analysed. A few experiments are conducted on two widely used HS data sets, Pavia University and Salinas. The results demonstrate high capabilities of the proposed method in comparison with some state-of-the-art spatio-spectral HS classification methods.

Acknowledgments

The authors would like to thank Dr Ghamisi from the University of Iceland for providing the codes for ‘HMRFE SVM’ method. They also acknowledge Dr Jun Li from Sun Yat-Sen University for making the code for SVM CK and EMAP publically available.

Disclosure statement

No potential conflict of interest was reported by the authors.