Unsupervised selection of training plots and trees for tree species classification

In this study we introduced a novel unsupervised selection method for collecting training samples for tree species classification at individual tree crown (ITC) level using hyperspectral data. The selection process is based on a search strategy and a distance metric defined among the percentiles derived from the spectral distributions of the pixels inside the ITCs. The method was developed using two kinds of samples: i) plots, and ii) ITCs. The experimental results indicated that the method allows reducing the amount of training samples needed for the classification process, without significantly decreasing the classification accuracy.