Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 93, Issue , 2014, Pages 112-122

  Zabalza, Jaime ^a   Ren, Jinchang ^a   Yang, Mingqiang ^b   Zhang, Yi ^c   Wang, Jun ^d   Marshall, Stephen ^a   Han, Junwei ^e  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

^b SHANDONG UNIVERSITY   (China)

^c TIANJIN UNIVERSITY   (China)

^d BEIHANG UNIVERSITY   (China)

^e NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Data reduction; Feature extraction; Folded principal component analysis (F PCA); Hyperspectral imaging (HSI); Remote sensing; Support vector machine (SVM)

Indexed keywords

CLASSIFICATION (OF INFORMATION); COST BENEFIT ANALYSIS; COVARIANCE MATRIX; DATA REDUCTION; FEATURE EXTRACTION; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SPECTROSCOPY; SYNTHETIC APERTURE RADAR;

DATA CLASSIFICATION; FOLDED PRINCIPAL COMPONENT ANALYSIS (F-PCA); GLOBAL AND LOCAL STRUCTURES; HYPERSPECTRAL IMAGING; MEMORY REQUIREMENTS; OBJECTIVE EVALUATION; PRINCIPAL COMPONENTS ANALYSIS; QUANTITATIVE RESULT;

SUPPORT VECTOR MACHINES;

DATA SET; MATRIX; PHOTOGRAMMETRY; POLYMERASE CHAIN REACTION; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SPECTRAL ANALYSIS; SYNTHETIC APERTURE RADAR;

EID: 84899875503     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2014.04.006     Document Type: Article

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