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Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

1
Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China
2
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1600; https://doi.org/10.3390/rs10101600
Received: 5 September 2018 / Revised: 30 September 2018 / Accepted: 6 October 2018 / Published: 9 October 2018
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
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Abstract

Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods. View Full-Text
Keywords: additive white Gaussian noise (AWGN); hyperspectral images (HSIs); mixed noise; bandwise generalized bilinear model (BGBM); alternative direction method of multipliers (ADMM) additive white Gaussian noise (AWGN); hyperspectral images (HSIs); mixed noise; bandwise generalized bilinear model (BGBM); alternative direction method of multipliers (ADMM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, C.; Liu, Y.; Cheng, J.; Song, R.; Peng, H.; Chen, Q.; Chen, X. Hyperspectral Unmixing with Bandwise Generalized Bilinear Model. Remote Sens. 2018, 10, 1600.

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