NDLI: Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes

Content Provider	IEEE Xplore Digital Library
Author	Altmann, Y. Dobigeon, N. McLaughlin, S. Tourneret, J.
Copyright Year	2012
Description	Author affiliation: University of Toulouse, IRIT-ENSEEIHT, France (Altmann, Y.; Dobigeon, N.; Tourneret, J.) \|\| School of Engineering and Electronics, University of Edinburgh, U.K. (McLaughlin, S.)
Abstract	This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation procedure is applied simultaneously to all pixels of the image by maximizing an appropriate posterior distribution which does not depend on the endmembers. After determining the abundances of all image pixels, the endmembers contained in the image are estimated by using Gaussian process regression. The performance of the resulting unsupervised unmixing strategy is evaluated through simulations conducted on synthetic data.
Starting Page	1249
Ending Page	1252
File Size	237892
Page Count	4
File Format	PDF
ISBN	9781467300452
ISSN	15206149
e-ISBN	9781467300469
e-ISBN	9781467300445
DOI	10.1109/ICASSP.2012.6288115
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-25
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Estimation Hyperspectral imaging Gaussian processes Kernel Principal component analysis Gaussian Processes Nonlinear unmixing hyperspectral images
Content Type	Text
Resource Type	Article

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