Band Selection Using L-2,L-1-norm Regression for Hyperspectral Target Detection 초분광 표적 탐지를 위한 L2,1-norm Regression 기반 밴드 선택 기법

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When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the L-2,L-1-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.
Publisher
KOREAN SOC REMOTE SENSING
Issue Date
2017-10
Language
Korean
Article Type
Article
Citation

KOREAN JOURNAL OF REMOTE SENSING, v.33, no.5, pp.455 - 467

ISSN
1225-6161
DOI
10.7780/kjrs.2017.33.5.1.1
URI
http://hdl.handle.net/10203/240172
Appears in Collection
EE-Journal Papers(저널논문)
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