The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XXXIX-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 147–149, 2012
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 147–149, 2012

  31 Jul 2012

31 Jul 2012


G. J. Newnham1, D. Lazaridis2, N. C. Sims1, A. P. Robinson2, and D. S. Culvenor1 G. J. Newnham et al.
  • 1CSIRO Division of Land and Water and Sustainable Agriculture Flagship, Clayton South, Victoria, Australia
  • 2Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia

Keywords: Forest, Classification, Hyperspectral, Ensemble, Decision Tree, Random Forests

Abstract. The classification of vegetation in hyperspectral image scenes presents some challenges due to high band autocorrelations and problems dealing with many predictor variables. The Random Forests classification method is based on an ensemble of decision trees and attempts to address these issues by dealing with only a subset of image bands in each node of each decision tree. Random Forests has previously been used for classification of vegetation using hyperspectral data. However, the variable importance measure that is a by-product of the technique has largely been ignored. In this study we investigate the spectral qualities of variable importance in the classification of forest and non-forest in a single Hyperion scene. The spectral importance curve showed broad bands of importance over wavelength regions known to be significant in biochemical absorption.