Band Selection Using Sparse Self-representation for Hyperspectral Imagery

SUN Weiwei; JIANG Man; LI Weiyue

doi:10.13203/j.whugis20150052

Volume 42 Issue 4

Apr. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(4): 441-448. > DOI: 10.13203/j.whugis20150052

SUN Weiwei, JIANG Man, LI Weiyue. Band Selection Using Sparse Self-representation for Hyperspectral Imagery[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4): 441-448. DOI: 10.13203/j.whugis20150052

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Band Selection Using Sparse Self-representation for Hyperspectral Imagery

SUN Weiwei^{1, 2, 3,},
JIANG Man²,
LI Weiyue^4, ,

1.
Key Laboratory for Aerial Remote Sensing Technology of National Administration of Surverying, Mapping and Geoinformation (NASMG), Beijing 100830, China
2.
Faculty of Architectural Engineering, Civil Engineering and Environment, Ningbo University, Ningbo 315211, China
3.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
4.
Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China

Funds:

The National Natural Science Foundation of China Nos. 41671342, 41401389

the Key Laboratory for Aerial Remote Sensing Technology of National Administration of Surveying, Mapping and Geoinformation (NASMG) No. 2015B12

the Chinese Postdoctoral Science Foundation Nos. 2016T90732, 2015M570668

Public Projects of Zhejiang Province No. 2016C33021

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Author Bio:
SUN Weiwei: SUN weiwei, PhD, associate professor, specializes in the theory and techniques of GIS and remote sensing, and “3S” applications in Coastal resources management and monitoring. E-mail:sunweiwei@nbu.edu.cn
Corresponding author:
LI Weiyue, PhD. E-mail:lwy_326@126.com
Received Date: January 29, 2015
Published Date: April 04, 2017

Graphical Abstract

Abstract

Abstract

Hyperspectral imaging could collect spectrum information of ground objects on the earth surface using hundreds of bands and are widely used in recognizing subtle differences among difference ground objects. Unfortunately, numerous bands with strong intra-band correlations cause unbearable computational burdens in hyperspectral processing, and especially that seriously hinders the classification of Hyperspectral imagery (HSI) in many realistic applications. Therefore, a sparse self-representation (SSR) method was proposed to select proper bands and make dimensionality reduction on HSI data to benefit its further classification procedure. The SSR improves the sparse representation model of multiple measurement vectors (MMV) using the idea that the dictionary matrix is equal to the measurement matrix, and it regards the aimed band subset as the representative from all bands of the HSI dataset. The method formulates the band selection into finding nonzero row vectors of sparse coefficient matrix in MMV, and adopts the mixed norm to constrain the number of nonzero row vectors. The sparse coefficient matrix is solved by using fast alternating direction method of multipliers and nonzero row vectors are clustered to make proper selection from all bands. Two open HSI datasets including Urban and Pavia University are implemented to testify our SSR method and the results are compared with the other four alternative band selection methods. Experimental results show that the SSR achieves comparable even better overall classification accuracies than the linear constrained minimum variance-based band correlation constraint (LCMV-BCC) algorithm and the sparse nonnegative matrix factorization (SNMF) algorithm, whereas the computational speed of SSR significantly outperforms that of LCMV-BCC. The proposed SSR could accordingly be a good alternative to help choose proper bands from hyperspectral images.

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References

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