Blind separation of sources, Part II: problems statement
Signal Processing
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Bayesian curve fitting using MCMC with applications to signalsegmentation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
A Bayesian Approach for Blind Separation of Sparse Sources
IEEE Transactions on Audio, Speech, and Language Processing
Super-resolution reconstruction of hyperspectral images
IEEE Transactions on Image Processing
A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing
IEEE Transactions on Signal Processing
Bayesian orthogonal component analysis for sparse representation
IEEE Transactions on Signal Processing
Lattice independent component analysis for functional magnetic resonance imaging
Information Sciences: an International Journal
Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior
Journal of Signal Processing Systems
Coverage segmentation based on linear unmixing and minimization of perimeter and boundary thickness
Pattern Recognition Letters
Hybrid computational methods for hyperspectral image analysis
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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This paper studies a fully Bayesian algorithm for end-member 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.