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Proceedings Paper

Classification extraction of land coverage in the Ejina Oasis by airborne hyperspectral remote sensing
Author(s): Yang Su; Yuan Qi; Jianhua Wang; Feinan Xu; Jinlong Zhang
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Paper Abstract

The hyperspectral data of the Ejina Oasis is performed dimensionality reduction by the minimum noise fraction transform, classified by the maximum likelihood method and clustering processing and finally, the classification of land coverage is obtained. The thesis analyzes the results of two dimensionality reductions, and finds the superiority of the minimum noise fraction. In the classification of land coverage, the overall accuracy and Kappa coefficient are respectively 87.75% and 0.8401. The classification is of high precision and can provide effective parameters for ecological research.

Paper Details

Date Published: 8 March 2017
PDF: 9 pages
Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 102551W (8 March 2017); doi: 10.1117/12.2264804
Show Author Affiliations
Yang Su, Northwest Institute of Eco-Environment and Resources (China)
Univ. of Chinese Academy of Sciences (China)
Yuan Qi, Northwest Institute of Eco-Environment and Resources (China)
Jianhua Wang, Northwest Institute of Eco-Environment and Resources (China)
Feinan Xu, Northwest Institute of Eco-Environment and Resources (China)
Jinlong Zhang, Northwest Institute of Eco-Environment and Resources (China)


Published in SPIE Proceedings Vol. 10255:
Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016
Yueguang Lv; Jialing Le; Hesheng Chen; Jianyu Wang; Jianda Shao, Editor(s)

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