Paper
24 August 2010 High-order statistics Harsanyi-Farrand-Chang method for estimation of virtual dimensionality
Author Affiliations +
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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chein-I Chang and Wei Xiong "High-order statistics Harsanyi-Farrand-Chang method for estimation of virtual dimensionality", Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 78100D (24 August 2010); https://doi.org/10.1117/12.865193
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Principal component analysis

Independent component analysis

Statistical analysis

Sensors

Minerals

Interference (communication)

Binary data

Back to Top