Sensors & Transducers Journal
(ISSN 1726- 5479)
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205.767 |
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(ISSN 1726- 5479) |
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Vol. 156, Issue 9, September 2013, pp. 298-303
Semi-Supervised Based Hyperspectral Imagery ClassificationZhijun Zheng, Yanbin Peng
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, 318 Liuhe Road, Xihu District, Hangzhou City,
Received: 5 June 2013 /Accepted: 25 August 2013 /Published: 30 September 2013 |
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Abstract: Hyperspectral imagery classification is a challenging problem. Wherein, the high number of spectral channels and the high cost of true sample labeling greatly reduce the classification precision. In this paper, we proposed a semi-supervised method, which combine linear discriminant analysis and manifold learning, to improve the precision of hyperspectral imagery classification. Experimental results showed that new method had provided considerable insight on the band extraction problem and the new features were good for land-cover classification.
Keywords: Hyperspectral imagery, Classification, Semi-supervised, Linear discriminant analysis, Manifold learning.
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