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Proceedings Paper

On the performance of virtual dimensionality estimation for hyperspectral image analysis
Author(s): Narreenart Raksuntorn; Qian Du
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Paper Abstract

The concept of virtual dimensionality (VD) has been developed for estimating the number of spectrally distinctive signals in a hyperspectral image. It has important applications in hyperspectral image analysis. For instance, it is related to the number of classes in classification and the number of endmembers in linear mixture analysis; an appropriate VD estimate will facilitate the related algorithm implementation and improve their performance. In this paper, we will evaluate several VD estimation approaches, including a Neyman-Pearson Detection based method and a Signal Subspace Estimation based method. In particular, we will discuss how the noise estimation affects the accuracy of VD estimate.

Paper Details

Date Published: 10 October 2008
PDF: 8 pages
Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 71090E (10 October 2008); doi: 10.1117/12.800249
Show Author Affiliations
Narreenart Raksuntorn, Mississippi State Univ. (United States)
Qian Du, Mississippi State Univ. (United States)

Published in SPIE Proceedings Vol. 7109:
Image and Signal Processing for Remote Sensing XIV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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