Penalized Linear Discriminant Analysis of in Situ Hyperspectral Data for Conifer Species Recognition

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hyperspectral imaging, linear discriminant analysis, hyperspectral sensors, resource management, large-scale systems, biochemistry, soil measurements, artificial neural networks, statistical analysis, protection

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Using in situ hyperspectral measurements collected in the Sierra Nevada Mountains in California, the authors discriminate six species of conifer trees using a recent, nonparametric statistics technique known as penalized discriminant analysis (PDA). A classification accuracy of 76% is obtained. Their emphasis is on providing an intuitive, geometric description of PDA that makes the advantages of penalization clear. PDA is a penalized version of Fisher's linear discriminant analysis (LDA) and can greatly improve upon LDA when there are a large number of highly correlated variables.

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IEEE Transactions on Geoscience and Remote Sensing, v. 37, issue 5, p. 2569-2577