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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2016, Vol. 10 Issue (2) : 292-301    https://doi.org/10.1007/s11704-015-4103-4
RESEARCH ARTICLE
Generating labeled samples for hyperspectral image classification using correlation of spectral bands
Lu YU1,2,Jun XIE3,Songcan CHEN2,*(),Lei ZHU1
1. Institute of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China
2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
3. College of Command Information System, PLA University of Science and Technology, Nanjing 210007, China
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Abstract

Because the labor needed to manually label a huge training sample set is usually not available, the problem of hyperspectral image classification often suffers from a lack of labeled training samples. At the same time, hyperspectral data represented in a large number of bands are usually highly correlated. In this paper, to overcome the small sample problem in hyperspectral image classification, correlation of spectral bands is fully utilized to generate multiple new sub-samples from each original sample. The number of labeled training samples is thus increased several times. Experiment results demonstrate that the proposed method has an obvious advantage when the number of labeled samples is small.

Keywords hyperspectral image      remote sensing      image classification      small sample problem     
Corresponding Author(s): Songcan CHEN   
Just Accepted Date: 15 June 2015   Issue Date: 16 March 2016
 Cite this article:   
Lu YU,Jun XIE,Songcan CHEN, et al. Generating labeled samples for hyperspectral image classification using correlation of spectral bands[J]. Front. Comput. Sci., 2016, 10(2): 292-301.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-015-4103-4
https://academic.hep.com.cn/fcs/EN/Y2016/V10/I2/292
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