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

摘要

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\u27s linear discriminant analysis (LDA) and can greatly improve upon LDA when there are a large number of highly correlated variables.