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Proceedings Paper

Hyperspectral image classification using spectral histograms and semi-supervised learning
Author(s): Sol M. Cruz Rivera; Vidya Manian
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Paper Abstract

In this paper, an algorithm that extracts regional texture information by computing spectral difference histograms over window extents in hyperspectral images is presented. The spectral angle distance is used as the spectral metric and different window sizes are explored for computing the histogram. The histograms are used in a semi-supervised learning framework that uses both labeled and unlabeled samples for training the support vector machine classifier, which is then tested with unlabeled samples. Results are presented with real and synthetic hyperspectral images. The method performs well with high spatial resolution images. The algorithm performs well under different noise levels.

Paper Details

Date Published: 11 April 2008
PDF: 12 pages
Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660G (11 April 2008); doi: 10.1117/12.778222
Show Author Affiliations
Sol M. Cruz Rivera, Univ. of Puerto Rico at Mayagüez (United States)
Vidya Manian, Univ. of Puerto Rico at Mayagüez (United States)


Published in SPIE Proceedings Vol. 6966:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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