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Proceedings Paper

Statistics enhancement in hyperspectral data analysis using spectral-spatial labeling, the EM algorithm, and the leave-one-out covariance estimator
Author(s): Pi-Fuei Hsieh; David A. Landgrebe
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Paper Abstract

Hyperspectral data potentially contain more information than multispectral data because of higher dimensionality. Information extraction algorithm performance is strongly related to the quantitative precision with which the desired classes are defined, a characteristic which increase rapidly with dimensionality. Due to the limited number of training samples used in defining classes, the information extraction of hyperspectral data may not perform as well as needed. In this paper, schemes for statistics enhancement are investigated for alleviating this problem. Previous works including the EM algorithm and the Leave-One-Out covariance estimator are discussed. The HALF covariance estimator is proposed for two-class problems by using the symmetry property of the normal distribution. A spectral-spatial labeling scheme is proposed to increase the training sample sizes automatically. We also seek to combine previous works with the proposed methods so as to take full advantage of statistics enhancement. Using these techniques, improvement in classification accuracy has been observed.

Paper Details

Date Published: 16 October 1998
PDF: 8 pages
Proc. SPIE 3438, Imaging Spectrometry IV, (16 October 1998); doi: 10.1117/12.328101
Show Author Affiliations
Pi-Fuei Hsieh, Purdue Univ. (China)
David A. Landgrebe, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 3438:
Imaging Spectrometry IV
Michael R. Descour; Sylvia S. Shen, Editor(s)

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