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 language | American English |
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Journal | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 37 |
DOIs | |
State | Published - Jan 1 1999 |
Externally published | Yes |
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