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Optical Engineering

Semi-supervised dimensionality reduction using orthogonal projection divergence-based clustering for hyperspectral imagery
Author(s): Hongjun Su; Peijun Du; Qian Du
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Paper 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.

Paper Details

Date Published: 17 July 2012
PDF: 9 pages
Opt. Eng. 51(11) 111715 doi: 10.1117/1.OE.51.11.111715
Published in: Optical Engineering Volume 51, Issue 11
Show Author Affiliations
Hongjun Su, Nanjing Univ. (China)
Peijun Du, Nanjing Univ. (China)
Qian Du, Mississippi State Univ. (United States)

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