Title

Analysis of Hyperspectral Data with Diffusion Maps and Fuzzy ART

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.

Meeting Name

International Joint Conference on Neural Networks, IJCNN 2009 (2009: Jun. 14-19, Atlanta, GA)

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

Missouri University of Science and Technology. Applied Computational Intelligence Laboratory

International Standard Book Number (ISBN)

9781424435531

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2009 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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