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Vol. 156, Issue 9, September 2013, pp. 298-303

 

Bullet

 

Semi-Supervised Based Hyperspectral Imagery Classification
 
Zhijun Zheng, Yanbin Peng

School of Information and Electronic Engineering, Zhejiang University of Science and Technology, 318 Liuhe Road, Xihu District, Hangzhou City,
Zhejiang Province, 310023, P. R. China
E-mail: zjzheng9999@163.com

 

Received: 5 June 2013   /Accepted: 25 August 2013   /Published: 30 September 2013

Digital Sensors and Sensor Sysstems

 

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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