Explicit signal to noise ratio in reproducing kernel Hilbert spaces

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn , Issue , 2011, Pages 3570-3573

  Gómez Chova, Luis ^a   Nielsen, Allan A ^b   Camps Valls, Gustavo ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

^b TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

feature extraction; Kernel methods; kernel minimum noise fraction; kernel principal component analysis; signal to noise ratio

Indexed keywords

HYPERSPECTRAL IMAGE CLASSIFICATION; KERNEL METHODS; KERNEL PRINCIPAL COMPONENT ANALYSIS; MINIMUM NOISE FRACTION; NOISE VARIANCE; NONLINEAR FEATURE EXTRACTION METHOD; NONLINEAR RELATIONS; REMOTE SENSING DATA; REPRODUCING KERNEL HILBERT SPACES; SIGNAL FEATURES; SIGNAL TO NOISE; SIGNAL VARIANCE;

COMPUTATION THEORY; DATA REDUCTION; FEATURE EXTRACTION; GEOLOGY; LEARNING ALGORITHMS; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SIGNAL TO NOISE RATIO; SPACE OPTICS;

HILBERT SPACES;

EID: 80955159602     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2011.6049993     Document Type: Conference Paper

References (5)

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2. G. Camps-Valls and L. Bruzzone, Eds., Kernel methods for Remote Sensing Data Analysis, Wiley & Sons, UK, Dec 2009.
3. A. A. Nielsen, "Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Trans. Image Processing, vol. 20, no. 3, pp. 612-624, Mar. 2011.
4. I. T. Jolliffe, Principal Component Analysis, Springer, 1986.
5. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.