A theory of high-order statistics-based virtual dimensionality for hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing

Volumn 52, Issue 1, 2014, Pages 188-208

  Chang, Chein I ^a,b,c   Xiong, Wei ^a   Wen, Chia Hsien ^b  

^a Baltimore County   (United States)

^b PROVIDENCE UNIVERSITY   (Taiwan)

^c NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Harsanyi Farrand Chang (HFC) method; High order statistics (HOS); High order statistics based Harsanyi Farrand Chang (HOS HFC) method; Highorder statistics based VD (HOS VD) theory; Maximum orthogonal complement algorithm (MOCA); Neyman Pearson detection (NPD); Second order statistics based VD (2OS) theory; Virtual dimensionality (VD)

Indexed keywords

HARSANYI-FARRAND-CHANG (HFC) METHOD; HIGH ORDER STATISTICS; HIGH-ORDER; NEYMAN PEARSON DETECTION; ORTHOGONAL COMPLEMENTS; SECOND ORDERS; VIRTUAL DIMENSIONALITY;

EIGENVALUES AND EIGENFUNCTIONS; POLYNOMIALS;

SPECTROSCOPY;

ALGORITHM; DATA SET; SPECTRAL ANALYSIS; STATISTICAL ANALYSIS; THEORETICAL STUDY;

EID: 84890439704     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2012.2237554     Document Type: Article

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