Dynamic classifier selection using spectral-spatial information for hyperspectral image classification

Hongjun Su; Bin Yong; Peijun Du; Hao Liu; Chen Chen; Kui Liu

doi:10.1117/1.JRS.8.085095

22 August 2014 Dynamic classifier selection using spectral-spatial information for hyperspectral image classification

Hongjun Su, Bin Yong, Peijun Du, Hao Liu, Chen Chen, Kui Liu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 085095 (August 2014). https://doi.org/10.1117/1.JRS.8.085095

Abstract

This paper presents a dynamic classifier selection approach for hyperspectral image classification, in which both spatial and spectral information are used to determine a pixel’s label once the remaining classified pixels’ neighborhood meets the threshold. For volumetric texture feature extraction, a volumetric gray level co-occurrence matrix is used; for spectral feature extraction, a minimum estimated abundance covariance-based band selection is used. Two hyperspectral remote sensing datasets, HYDICE Washington DC Mall and AVIRIS Indian Pines, are employed to evaluate the performance of the developed method. The classification accuracies of the two datasets are improved by 1.13% and 4.47%, respectively, compared with the traditional algorithms using spectral information. The experimental results demonstrate that the integration of spectral information with volumetric textural features can improve the classification performance for hyperspectral images.

Citation Download Citation

Hongjun Su, Bin Yong, Peijun Du, Hao Liu, Chen Chen, and Kui Liu "Dynamic classifier selection using spectral-spatial information for hyperspectral image classification," Journal of Applied Remote Sensing 8(1), 085095 (22 August 2014). https://doi.org/10.1117/1.JRS.8.085095

Published: 22 August 2014

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