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Proceedings Paper

Unsupervised hyperspectral image classification
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Paper Abstract

Two major issues encountered in unsupervised hyperspectral image classification are (1) how to determine the number of spectral classes in the image and (2) how to find training samples that well represent each of spectral classes without prior knowledge. A recently developed concept, Virtual dimensionality (VD) is used to estimate the number of spectral classes of interest in the image data. This paper proposes an effective algorithm to generate an appropriate training set via a recently developed Prioritized Independent Component Analysis (PICA). Two sets of hyperspectral data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Cuprite data and HYperspectral Digital Image Collection Experiment (HYDICE) data are used for experiments and performance analysis for the proposed method.

Paper Details

Date Published: 12 September 2007
PDF: 10 pages
Proc. SPIE 6661, Imaging Spectrometry XII, 66610I (12 September 2007); doi: 10.1117/12.732614
Show Author Affiliations
Xiaoli Jiao, Univ. of Maryland, Baltimore County (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)

Published in SPIE Proceedings Vol. 6661:
Imaging Spectrometry XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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