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Hyperspectral Unmixing with Bandwise Generalized Bilinear Model Li, Chang; Liu, Yu; Cheng, Juan; Song, Rencheng; Peng, Hu; Chen, Qiang; Chen, Xun

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.

Item Metadata

Title
Hyperspectral Unmixing with Bandwise Generalized Bilinear Model
Creator
Li, Chang; Liu, Yu; Cheng, Juan; Song, Rencheng; Peng, Hu; Chen, Qiang; Chen, Xun
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2018-10-09
Description
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.
Subject
Additive white Gaussian noise (AWGN); Hyperspectral images (HSIs); Mixed noise; Bandwise generalized bilinear model (BGBM); Alternative direction method of multipliers (ADMM)
Genre
Article
Type
Text
Language
eng
Date Available
2019-06-18
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0379501
URI
http://hdl.handle.net/2429/70687
Affiliation
Applied Science, Faculty of; Non UBC; Electrical and Computer Engineering, Department of
Citation
Remote Sensing 10 (10): 1600 (2018)
Publisher DOI
10.3390/rs10101600
Peer Review Status
Reviewed
Scholarly Level
Faculty
Rights URI
https://creativecommons.org/licenses/by/4.0/
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CC BY 4.0