Hyperspectral image classification based on volumetric texture and dimensionality reduction

Abstract

A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural features were extracted by volumetric gray-level co-occurrence matrices (VGLCM). The spectral features were extracted by minimum estimated abundance covariance (MEAC) and linear prediction (LP)-based band selection, and a semi-supervised k-means (SKM) clustering method with deleting the worst cluster (SKMd) band-clustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classification by using spectral and textural features. It has been proven that the proposed method using VGLCM outperforms the gray-level co-occurrence matrices (GLCM) method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.

Keywords

hyperspectral imagery / image classification / volumetric textural feature / spectral feature / fusion

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Acknowledgements

This paper was partially supported by the National Natural Science Foundation of China (Grant No. 41201341), the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (Grant No. 12R02), Key Laboratory of Satellite Mapping Technology and Application, National Administration of Surveying, Mapping and Geoinformation (Grant No. KLSMTA-201301), Key Laboratory of Advanced Engineering Surveying of National Administration of Surveying, Mapping and Geoinformation (Grant No. TJES1301), and the Scientific Research Foundation for Jiangsu Key Laboratory of Resources and Environmental Information Engineering (Grant No. JS201303).

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg