@inproceedings{a6050acc2f6f407e8c6826e0a0087b95,
title = "A hyperspectral classification method based on experimental model of vegetation parameters and C5.0 decision tree of multiple combined classifiers",
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.",
keywords = "C5.0 decision tree, Hyperspectral classification, Multiple classifiers, Remote sensing, Vegetation biochemistry parameter",
author = "Xuemei Gong and Juan Lin and Kun Gao and Liu Ying and Meng Wang",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, OIT 2015 ; Conference date: 17-05-2015 Through 19-05-2015",
year = "2015",
doi = "10.1117/12.2185000",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Guangming Shi and Bormin Huang and Xuelong Li",
booktitle = "2015 International Conference on Optical Instruments and Technology",
address = "United States",
}