Generating labeled samples for hyperspectral image classification using correlation of spectral bands

doi:10.1007/s11704-015-4103-4

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 YU^1,²,Jun XIE³,Songcan CHEN^2,^*(

),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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