Comparison of support vector machine classification to partial least squares dimension reduction with logistic discrimination of hyperspectral data

Machelle D. Wilson, Susan L. Ustin, David M Rocke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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 languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsM. Ehlers
Pages487-497
Number of pages11
Volume4886
DOIs
StatePublished - 2002
EventRemote Sensing for Environmental Monitoring, GIS Applications, and Geology II - Agia Pelagia, Greece
Duration: Sep 23 2002Sep 26 2002

Other

OtherRemote Sensing for Environmental Monitoring, GIS Applications, and Geology II
CountryGreece
CityAgia Pelagia
Period9/23/029/26/02

Keywords

  • Heavy metals
  • Hyperspectral
  • Logistic discrimination
  • Partial least squares
  • Reflectance
  • Support vector machines

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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    Wilson, M. D., Ustin, S. L., & Rocke, D. M. (2002). Comparison of support vector machine classification to partial least squares dimension reduction with logistic discrimination of hyperspectral data. In M. Ehlers (Ed.), Proceedings of SPIE - The International Society for Optical Engineering (Vol. 4886, pp. 487-497) https://doi.org/10.1117/12.463169