Analysis of Hyperspectral Data with Diffusion Maps and Fuzzy ART
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.
R. Xu et al., "Analysis of Hyperspectral Data with Diffusion Maps and Fuzzy ART," Proceedings of the International Joint Conference on Neural Networks, pp. 3390-3397, Institute of Electrical and Electronics Engineers (IEEE), Jan 2009.
The definitive version is available at https://doi.org/10.1109/IJCNN.2009.5178910
International Joint Conference on Neural Networks, IJCNN 2009 (2009: Jun. 14-19, Atlanta, GA)
Electrical and Computer Engineering
Missouri University of Science and Technology. Applied Computational Intelligence Laboratory
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2009 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.