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Proceedings Paper

A hyperspectral classification method based on experimental model of vegetation parameters and C5.0 decision tree of multiple combined classifiers
Author(s): Xuemei Gong; Juan Lin; Kun Gao; Liu Ying; Meng Wang
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Paper Abstract

To meet the requirement of fine vegetation classification in hyperspectral remote sensing applications, an improved method based on C5.0 decision tree of multiple combined classifiers is proposed. It consists of 2 classification stages: rough classification and fine classification. During the first stage, experimental model is used to estimate vegetation biochemistry parameters. Then 3 supervised classifiers, namely Spectral Angle Mapping, Minimum Distance, and Maximum Likelihood, combined by C5.0 decision tree, are used to realize the final fine classification. Experiments show that comparing with the traditional mono-classification algorithms, the proposed method can reduce the classification error effectively and more suitable for the vegetation investigation in the hyperspectral remote sensing applications.

Paper Details

Date Published: 5 August 2015
PDF: 11 pages
Proc. SPIE 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 96220B (5 August 2015); doi: 10.1117/12.2185000
Show Author Affiliations
Xuemei Gong, Beijing Institute of Technology (China)
Juan Lin, Beijing Institute of Technology (China)
Kun Gao, Beijing Institute of Technology (China)
Liu Ying, Beijing Institute of Technology (China)
Meng Wang, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 9622:
2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
Guangming Shi; Xuelong Li; Bormin Huang, Editor(s)

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