Analysis of hyperspectral data with diffusion maps and fuzzy ART

Authors:
Rui Xu;Louis du Plessis;Steven Damelin;Michael Sears;Donald C. Wunsch
Affiliations:
Applied Computational Intelligence Laboratory, Department of Electrical & Computer Engineering, Missouri University of Science and Technology, MO;School of Computational and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa;Department of Mathematical Sciences, Georgia Southern University, Statesboro, GA and School of Computational and Applied Mathematics, University of the Witwatersrand, Johanne sburg, South Africa;School of Computer Science, University of the Witwatersrand, Johannesburg, South Africa;Department of Electrical & Computer Engineering , Missouri University of Science & Technology, Rolla, MO
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

The presence of large amounts of data in hyperspectral images makes it very difficult to perform further tractable analyses. Here, we present a method of analyzing real hyperspectral data by dimensionality reduction using diffusion maps. Diffusion maps interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain an efficient representation of data geometric descriptions. A neural network clustering theory, Fuzzy ART, is further applied to the reduced data to form clusters of the potential minerals. Experimental results on a subset of hyperspectral core imager data show that the proposed methods are promising in addressing the complicated hyperspectral data and identifying the minerals in core samples.