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

B. Yu, M. Ostland, Peng Gong, Ruiliang Pu

Research output: Contribution to journalArticlepeer-review

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.

Original languageAmerican English
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume37
DOIs
StatePublished - Jan 1 1999
Externally publishedYes

Keywords

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

Disciplines

  • Earth Sciences

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