Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery

By Nicolas Dobigeon, Saïd Moussaoui, Martial Coulon, Jean-Yves Tourneret, A. Hero. IEEE Transactions on Signal Processing, 2009

390 citations recorded by the source corpus.

This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for nonnegativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images.

Subfields: Analytical Chemistry, Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/dd489b78bbb2fb45a07d5af2586287639146b9ff

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Citations in the corpus

Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis (2012, background): “In many situations, the spatial resolution may not be fine enough to resolve the object of interest on ground, and inadvertent mixing between multiple classes may occur [33].”
Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms (2013, methodology): “parison with supervised unmixing, and to evaluate the impact of having no pure pixels in these images, joint estimations of endmembers and abundances was implemented using the Markov chain Monte Carlo techniques detailed in [56] and [39] for the LMM and PPNM images, respectively.”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, neutral): “Note that in [162], [163] and [164] independent uniform distributions over the admissible simplex are chosen as prior distributions for the abundance vectors.”
Nonlinear unmixing of hyperspectral images using a generalized bilinear model (2011, background): “The ﬁrst hyperspectral image has received much attention in the remote sensing community [25], [30].”
A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing (2014, neutral): “We will not cover Bayesian techniques [8], although readers should note that they also represent key developments in blind HU.”