Regularization of discriminant analysis for the study of biodiversity in humid tropical forests

Jean Baptiste Feret, Gregory P. Asner, Stephane Jacquemoud

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


The performance of two supervised classifiers, linear and regularized discriminant analysis (LDA and RDA), is compared here for canopy species discrimination in humid tropical forest, based on airborne hyperspectral imagery acquired with the sensor Carnegie Airborne Observatory Alpha System (CAO-Alpha). Classification is performed to identify 13 species at pixel scale, crown scale, and using an object-based approach. The results show that for each scale of study, 70% to 75% overall accuracy is obtained with LDA. RDA allows improved classification for more than half species, and 5% increase of overall accuracy compared to LDA. The extended spectral range of the forthcoming CAO AToMS system (380-2500 nm) will allow for even more accurate classifications of tropical canopy species.

Original languageEnglish (US)
Article number6080945
JournalWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
StatePublished - 2011
Externally publishedYes
Event3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2011 - Lisbon, Portugal
Duration: Jun 6 2011Jun 9 2011


  • CAO
  • Discriminant Analysis
  • Humid Tropical Forests
  • Image Classification
  • Regularization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing


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