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- W2051220684 abstract "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." @default.
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- W2051220684 date "2013-07-01" @default.
- W2051220684 modified "2023-09-23" @default.
- W2051220684 title "Unsupervised selection of training plots and trees for tree species classification" @default.
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- W2051220684 doi "https://doi.org/10.1109/igarss.2013.6723225" @default.
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