Separable Nonnegative Matrix Factorization Based Band Selection for Hyperspectral Imagery

YANG Gang; SUN Weiwei; ZHANG Dianfa

doi:10.13203/j.whugis20170174

Volume 44 Issue 5

May 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(5): 737-744. > DOI: 10.13203/j.whugis20170174

YANG Gang, SUN Weiwei, ZHANG Dianfa. Separable Nonnegative Matrix Factorization Based Band Selection for Hyperspectral Imagery[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 737-744. DOI: 10.13203/j.whugis20170174

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Separable Nonnegative Matrix Factorization Based Band Selection for Hyperspectral Imagery

YANG Gang^1,,
SUN Weiwei^{1, 2, ,},
ZHANG Dianfa¹

1.
Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China
2.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Funds:

National Natural Science Foundation of China 41671342

National Natural Science Foundation of China 41801256

Natural Science Foundation of Zhejiang Province LR19D010001

Natural Science Foundation of Zhejiang Province LQ18D010001

Open Fund of Key Laboratory of Earth Observation and Geospatial Information Science of NASG 201804

Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University 18R05

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Author Bio:
YANG Gang, PhD, lecturer, specializes in quality improvement of remote sensing and information extraction, and coastal health monitoring techniques and application of remote sensing. E-mail: yanggang@nbu.edu.cn
Corresponding author:
SUN Weiwei, PhD, associate professor. E-mail: sunweiwei@nbu.edu.cn
Received Date: April 16, 2018
Published Date: May 04, 2019

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Abstract

Abstract

Strong intra-band correlations along with numerous bands seriously hinder the processing and applications of hyperspectral remote sensing images in realistic applications. A separable non-negative matrix factorization (SepNMF) method is presented to explore the band selection problem on hyperspectral imagery (HSI). The method investigates the separability structure in the band set of the HSI data to improve the regular non-negative matrix factorization model, and it formulates the band selection problem into the problem of finding representative columns that represent other bands with non-negative and linear combinations in the SepNMF model. The method adopts the recursive projection method to iteratively select the representative bands to constitute the proper band subset. Three groups of experiments on two open HSI data sets are designed to carefully testify the performance of the SepNMF in band selection. Several popular methods are utilized to compare against the proposed SepNMF method. Experimental results show that the SepNMF obtains the best overall classification accuracies of all while taking shorter computational times ranking second among all the comparison methods. Therefore, the SepNMF method can be an alternative choice for selecting proper bands in hyperspectral image classification.
- hyperspectral imagery,
- separable nonnegative matrix factorization,
- band selection,
- recursive projection

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References (26)

References

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