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5 August 2015 A hyperspectral classification method based on experimental model of vegetation parameters and C5.0 decision tree of multiple combined classifiers
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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.
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Xuemei Gong, Juan Lin, Kun Gao, Liu Ying, and Meng Wang "A hyperspectral classification method based on experimental model of vegetation parameters and C5.0 decision tree of multiple combined classifiers", Proc. SPIE 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 96220B (5 August 2015); https://doi.org/10.1117/12.2185000
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