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Journal of Applied Remote Sensing

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

Paper Details

Date Published: 22 August 2014
PDF: 14 pages
J. Appl. Rem. Sens. 8(1) 085095 doi: 10.1117/1.JRS.8.085095
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Hongjun Su, Hohai Univ. (China)
Nanjing Univ. (China)
Bin Yong, Hohai Univ. (China)
Peijun Du, Nanjing Univ. (China)
Hao Liu, Wuhan Univ. (China)
Chen Chen, The Univ. of Texas at Dallas (United States)
Kui Liu, The Univ. of Texas at Dallas (United States)

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