NDLI: Bilinear normal mixing model for spectral unmixing

Content Provider	IET Digital Library
Author	Luo, Wenfei Gao, Lianru Zhang, Ruihao Marinoni, Andrea Zhang, Bing
Abstract	Spectral unmixing (SU) is a useful tool for hyperspectral remote sensing image analysis. However, due to the interference of spectral variance and non-linearity caused by photon multiple-scattering, the result might be an inaccuracy. In addition, the unmixing performance of typically relies on the prior knowledge of endmembers. Although many classical endmember extraction algorithms have been presented, it is hard to obtain accurate endmembers in practical applications. This study presents a bilinear normal mixing model named as BNMM to tackle these issues. In fact, BNMM employs the polynomial post-non-linear mixing model to alleviate the effect of non-linearity and uses a normal distribution model to reduce the influence of endmembers variability. Based on the BNMM, the authors develop a Hamiltonian Monte Carlo algorithm for SU. The experimental results demonstrate that the proposed algorithm outperforms other classical unmixing algorithms in the case of simulated and benchmark datasets.
Starting Page	344
Ending Page	354
Page Count	11
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 2, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5458
Journal	IET Image Processing
Publisher Date	2018-11-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bilinear Normal Mixing Model BNMM Computer Vision And Image Processing Technique Data And Information; Acquisition, Processing, Storage And Dissemination in Geophysics Endmember Extraction Algorithm Endmembers Variability Feature Extraction Geography And Cartography Computing Geophysical Image Processing Geophysical Technique And Equipment Geophysics Computing Hamiltonian Monte Carlo Algorithm Hyperspectral Image Hyperspectral Remote Sensing Image Analysis Image Recognition Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Monte Carlo Method Nonlinearity Normal Distribution Normal Distribution Model Photon Multiple-scattering Polynomial Polynomial Postnonlinear Mixing Model Probability Theory Remote Sensing Spectral Analysis Spectral Unmixing Spectral Variance Statistics Stochastic Linearised SCUC Unmixing Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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