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Article

An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing

1
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
2
National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jaime Zabalza
Remote Sens. 2021, 13(19), 3941; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193941
Received: 25 August 2021 / Revised: 21 September 2021 / Accepted: 28 September 2021 / Published: 1 October 2021
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained. View Full-Text
Keywords: spectral variability; endmember bundle; spectral clustering spectral variability; endmember bundle; spectral clustering
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MDPI and ACS Style

Ye, C.; Liu, S.; Xu, M.; Du, B.; Wan, J.; Sheng, H. An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing. Remote Sens. 2021, 13, 3941. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193941

AMA Style

Ye C, Liu S, Xu M, Du B, Wan J, Sheng H. An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing. Remote Sensing. 2021; 13(19):3941. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193941

Chicago/Turabian Style

Ye, Chuanlong, Shanwei Liu, Mingming Xu, Bo Du, Jianhua Wan, and Hui Sheng. 2021. "An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing" Remote Sensing 13, no. 19: 3941. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193941

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