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Proceedings Paper

High-order statistics Harsanyi-Farrand-Chang method for estimation of virtual dimensionality
Author(s): Chein-I Chang; Wei Xiong
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Paper Abstract

Virtual dimensionality (VD) was introduced as a definition of the number of spectrally distinct signatures in hyperspectral data where a method developed by Harsanyi-Farrand-Chang, referred to as HFC method was used to estimate the VD. Unfortunately, some controversial issues occur due to misinterpretation of the VD. Since the non-literal (spectral) information is the most important and critical for hyperspectral data to be preserved, the VD is particularly defined to address this issue as the number of spectrally distinct signatures present in the data where each spectral dimension is used to accommodate one specific signature. With this interpretation the VD is actually defined as the minimum number of spectral dimensions used to characterize the hyperspectral data. In addition, since hyperspectral targets of interest are generally insignificant and their occurrences have low probabilities with small populations, their contributions to 2nd order statistics are usually very limited. Consequently, the HFC method using eigenvalues to determine the VD may not be applicable for this purpose. Therefore, this paper revisits the VD and extends the HFC method to high-order statistics HFC method to estimate the VD for such a type of hyperspectral targets present in the data.

Paper Details

Date Published: 24 August 2010
PDF: 12 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 78100D (24 August 2010); doi: 10.1117/12.865193
Show Author Affiliations
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)
National Chung Hsing Univ. (Taiwan)
Wei Xiong, Univ. of Maryland, Baltimore County (United States)


Published in SPIE Proceedings Vol. 7810:
Satellite Data Compression, Communications, and Processing VI
Bormin Huang; Antonio J. Plaza; Joan Serra-Sagristà; Chulhee Lee; Yunsong Li; Shen-En Qian, Editor(s)

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