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
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2011.2153861

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 Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. When the data are projected in an independent space, the estimates of their multivariate density function can be computed in a much easier way as the product of univariate densities. A nonparametric kernel density estimator is used to compute the density functions of each IC. Finally, the Bayes rule is applied for the classification assignment. In this paper, we investigate the possibility of using ICDA for the classification of hyperspectral images. We study the influence of the algorithm used to enforce independence and of the number of IC retained for the classification, proposing an effective method to estimate the most suitable number. The proposed method is applied to several hyperspectral images, in order to test different data set conditions (urban/agricultural area, size of the training set, and type of sensor). Obtained results are compared with one of the most commonly used classifier of hyperspectral images (support vector machines) and show the comparative effectiveness of the proposed method in terms of accuracy.