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1 Faculty of Electrical Engineering 2 GIPSA-SIGMAPHY - SIGMAPHY GIPSA-DIS - Département Images et Signal 3 University of Iceland Reykjavik 4 GIPSA-VIBS - VIBS GIPSA-DIS - Département Images et Signal

Abstract : 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.

Keywords : hyperspectral data Bayesian classification Independent Component IC Analysis ICA curse of dimensionality





Autor: Alberto Villa - Jon Atli Benediktsson - Jocelyn Chanussot - Christian Jutten -

Fuente: https://hal.archives-ouvertes.fr/



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