Penalized Linear Discriminant Analysis of in Situ Hyperspectral Data for Conifer Species Recognition
Document Type
Article
Publication Date
1999
Keywords
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)
https://doi.org/10.1109/36.789651
Abstract
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.
Was this content written or created while at USF?
No
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.
https://digitalcommons.usf.edu/geo_facpub/405
