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http://dx.doi.org/10.17663/JWR.2014.16.3.393

A study for Improvement the Accuracy of Tree Species Classification within Various Sizes of Training Sample Areas by Using the High-resolution Images  

Hou, Jin Sung (Department of Civil & Environmental Engineering, Kongju National University)
Yang, Keum Chul (Department of Civil & Environmental Engineering, Kongju National University)
Publication Information
Journal of Wetlands Research / v.16, no.3, 2014 , pp. 393-401 More about this Journal
Abstract
The purpose of this study was to investigate the objective impact in accuracy and reliability with tendency depend on training samples by using the high-resolution images. Supervised classification was performed based on multi-spectral images which made by each satellite and aerial images for considering all of bands' characteristics. The highest accuracy was 84.7% with satellite image(3*3) and 83% with aerial image(5*5) at the accuracy verification phase. Also, the overall accuracy with the consideration of Kappa coefficient were 0.84 for satellite images and 0.82 for aerial images. In all of the images, the smaller training sample was, the higher accuracy showed. Therefore, tree species classification accuracy was tended to rely on training sample size.
Keywords
Supervised classification; Digital numbers; Satellite imagery; Aerial photography;
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Times Cited By KSCI : 3  (Citation Analysis)
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