NDLI: Unsupervised Selection of Training Samples for Tree Species Classification Using Hyperspectral Data

Content Provider	IEEE Xplore Digital Library
Author	Dalponte, M. Ene, L.T. Orka, H.O. Gobakken, T. Naesset, E.
Copyright Year	2008
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 distance metric defined among the spectral signatures of the pixels inside the ITCs, and a search strategy based on the Sequential Forward Floating Selection algorithm. The method was developed using two kinds of samples: plots and 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. Applying the proposed method, the kappa accuracies obtained using about half of the total number of plots (kappa accuracy=0.84) and approximately one-third of the total number of ITCs (kappa accuracy=0.83) were not statistically different from the results obtained using the full set of training samples (kappa accuracy =0.86). The proposed method demonstrates that using a priori information derived from the hyperspectral data can substantially reduce the amount of field work and, consequently, the forest inventory costs.
Starting Page	3560
Ending Page	3569
Page Count	10
File Size	1787708
File Format	PDF
ISSN	19391404
Volume Number	7
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vegetation Training Measurement Accuracy Hyperspectral imaging Support vector machines unsupervised selection Classification forestry hyperspectral data training samples
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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