NDLI: Nonlinear spectral unmixing using residual component analysis and a Gamma Markov random field

Content Provider	IEEE Xplore Digital Library
Author	Altmann, Y. Pereyra, M. McLaughlin, S.
Copyright Year	2015
Description	Author affiliation: Sch. of Math., Univ. of Bristol, Bristol, UK (Pereyra, M.) \|\| Sch. of Eng. & Phys. Sci., Heriot-Watt Univ., Edinburgh, UK (Altmann, Y.; McLaughlin, S.)
Abstract	This paper presents a new Bayesian nonlinear unmixing model for hyperspectral images. The proposed model represents pixel reflectances as linear mixtures of end-members, corrupted by an additional combination of nonlinear terms (with respect to the end-members) and additive Gaussian noise. A central contribution of this work is to use a Gamma Markov random field to capture the spatial structure and correlations of the nonlinear terms, and by doing so to improve significantly estimation performance. In order to perform hyperspectral image unmixing, the Gamma Markov random field is embedded in a hierarchical Bayesian model representing the image observation process and prior knowledge, followed by inference with a Markov chain Monte Carlo algorithm that jointly estimates the model parameters of interest and marginalises latent variables. Simulations conducted with synthetic and real data show the accuracy of the proposed SU and nonlinearity estimation strategy for the analysis of hyperspectral images.
Starting Page	165
Ending Page	168
File Size	2431168
Page Count	4
File Format	PDF
e-ISBN	9781479919635
DOI	10.1109/CAMSAP.2015.7383762
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Bayesian estimation Computational modeling Hyperspectral imagery Estimation Markov processes nonlinear spectral unmixing Bayes methods Yttrium residual component analysis Gamma Markov random field Hyperspectral imaging
Content Type	Text
Resource Type	Article

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