Penalized Linear Discriminant Analysis of in Situ Hyperspectral Data for Conifer Species Recognition
hyperspectral imaging, linear discriminant analysis, hyperspectral sensors, resource management, large-scale systems, biochemistry, soil measurements, artificial neural networks, statistical analysis, protection
Digital Object Identifier (DOI)
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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Citation / Publisher Attribution
IEEE Transactions on Geoscience and Remote Sensing, v. 37, issue 5, p. 2569-2577
Scholar Commons Citation
Yu, B.; Ostland, M.; Gong, Peng; and Pu, Ruiliang, "Penalized Linear Discriminant Analysis of in Situ Hyperspectral Data for Conifer Species Recognition" (1999). School of Geosciences Faculty and Staff Publications. 405.