Semi-supervised dimensionality reduction using orthogonal projection divergence-based clustering for hyperspectral imagery

Hongjun Su; Peijun Du; Qian Du

doi:10.1117/1.OE.51.11.111715

17 July 2012 Semi-supervised dimensionality reduction using orthogonal projection divergence-based clustering for hyperspectral imagery

Hongjun Su, Peijun Du, Qian Du

Author Affiliations +

Optical Engineering, Vol. 51, Issue 11, 111715 (July 2012). https://doi.org/10.1117/1.OE.51.11.111715

Abstract

Band clustering and selection are applied to dimensionality reduction of hyperspectral imagery. The proposed method is based on a hierarchical clustering structure, which aims to group bands using an information or similarity measure. Specifically, the distance based on orthogonal projection divergence is used as a criterion for clustering. After clustering, a band selection step is applied to select representative band to be used in the following data analysis. Different from unsupervised clustering using all the pixels or supervised clustering requiring labeled pixels, the proposed semi-supervised band clustering and selection needs class spectral signatures only. The experimental results show that the proposed algorithm can significantly outperform other existing methods with regard to pixel-based classification task.

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Hongjun Su, Peijun Du, and Qian Du "Semi-supervised dimensionality reduction using orthogonal projection divergence-based clustering for hyperspectral imagery," Optical Engineering 51(11), 111715 (17 July 2012). https://doi.org/10.1117/1.OE.51.11.111715

Published: 17 July 2012

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