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Penalized discriminant analysis of in situ hyperspectral data for conifer species recognition

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4 Author(s)
Bin Yu ; Dept. of Stat., California Univ., Berkeley, CA, USA ; Ostland, M. ; Peng Gong ; Ruiliang Pu

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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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:37 ,  Issue: 5 )