Hyperspectral Image Classification With Independent Component Discriminant Analysis

• Published in: IEEE transactions on geoscience and remote sensing Vol. 49; no. 12; pp. 4865 - 4876
• Main Authors: Villa, A., Benediktsson, J. A., Chanussot, J., Jutten, C.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2011; Institute of Electrical and Electronics Engineers
• Subjects: Accuracy; Applied geophysics; Bayes methods; Bayesian classification; Computer Science; Covariance matrix; curse of dimensionality; Earth sciences; Earth, ocean, space; Engineering Sciences; Exact sciences and technology; hyperspectral data; Hyperspectral imaging; Independent Component (IC) Analysis (ICA); Independent component analysis; Integrated circuits; Internal geophysics; Signal and Image Processing; algorithms; hyperspectral data; discriminant analysis; projects; density; Bayesian classification; Independent Component (IC) Analysis (ICA); curse of dimensionality; accuracy; remote sensing; classification

In this paper, the use of Independent Component (IC) Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a nonparametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by ICs. The method uses IC Analysi...