Abstract
The classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from plants that have been exposed to varying levels of heavy metal toxicity was compared. The classification methods compared were support vector machine (SVM) classification of exposed and non-exposed plants based on the reflectance data, and partial least squares compression of the reflectance data followed by classification using logistics discrimination (PLS/LD). PLS/LD using binary predictor variables during compression had the lowest estimated prediction error for the data analyzed.
Original language | English (US) |
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Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
Editors | M. Ehlers |
Pages | 487-497 |
Number of pages | 11 |
Volume | 4886 |
DOIs | |
State | Published - 2002 |
Event | Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II - Agia Pelagia, Greece Duration: Sep 23 2002 → Sep 26 2002 |
Other
Other | Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II |
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Country | Greece |
City | Agia Pelagia |
Period | 9/23/02 → 9/26/02 |
Keywords
- Heavy metals
- Hyperspectral
- Logistic discrimination
- Partial least squares
- Reflectance
- Support vector machines
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics