Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis

This paper analyzes the classification of hyperspectral remote sensing images with linear discriminant analysis (LDA) in the presence of a small ratio between the number of training samples and the number of spectral features. In these particular ill-posed problems, a reliable LDA requires one to in...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2009-03, Vol.47 (3), p.862-873
Hauptverfasser:	Bandos, T.V., Bruzzone, L., Camps-Valls, G.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied geophysics Classification Communities Discriminant analysis Earth sciences Earth, ocean, space Exact sciences and technology Hyperspectral images Hyperspectral imaging Hyperspectral sensors Ill posed problems ill-posed problem Image analysis Image classification Internal geophysics Kernel Linear discriminant analysis linear discriminant analysis (LDA) Problem-solving Regularization Remote sensing Support vector machine classification Support vector machines Tuning
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