NDLI: Hyperspectral Feature Selection Based on Mutual Information and Nonlinear Correlation Coefficient

Content Provider	IEEE Xplore Digital Library
Author	Miao Zhang Qiang Wang Yi Shen Bo Zhang
Copyright Year	2009
Abstract	Mutual information (MI) has obvious potential for feature selection, but this has not been fully exploited in the past. In order to make numerical computation easier and more accurate, the MI of the whole multi-dimensional data can be decomposed into an amount of one-dimensional MI and one-dimensional conditional MI components. This paper reveals that using one-dimensional MI components to replace the one-dimensional conditional MI components may be problematic when the features are highly correlated, and we propose a method that using nonlinear correlation coefficient (NCC) to replace some one-dimensional MI components, which also including the conditional ones. Simulations are carried out on the AVIRIS 92AV3C dataset and the results show great potential for improvement in classification accuracy.
Starting Page	965
Ending Page	968
File Size	485481
Page Count	4
File Format	PDF
ISBN	9781424447176
DOI	10.1109/IIH-MSP.2009.71
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multidimensional signal processing Hyperspectral sensors Probability distribution Entropy Electronic mail nonlinear correlation coefficient hypersepctral data Infrared image sensors Feature extraction Random variables mutual information Mutual information feature selection Hyperspectral imaging
Content Type	Text
Resource Type	Article

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