A Bayesian approach to estimating linear mixtures with unknown covariance structure
暂无分享,去创建一个
[1] Chein-I Chang,et al. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..
[2] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[3] J. Zidek,et al. Inference for a covariance matrix , 1994 .
[4] Alfred O. Hero,et al. Bayesian linear unmixing of hyperspectral images corrupted by colored Gaussian noise with unknown covariance matrix , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[5] W. Verstraeten,et al. Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards , 2009 .
[6] V. P. Pauca,et al. Nonnegative matrix factorization for spectral data analysis , 2006 .
[7] Robert F. Cromp,et al. Analyzing hyperspectral data with independent component analysis , 1998, Other Conferences.
[8] Jan Larsen,et al. Bayesian nonnegative Matrix Factorization with volume prior for unmixing of hyperspectral images , 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.
[9] Roger N. Clark,et al. The US Geological Survey, digital spectral reflectance library: version 1: 0.2 to 3.0 microns , 1993 .
[10] José M. Bioucas-Dias,et al. Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.
[11] Chein-I Chang,et al. Semi-Supervised Linear Spectral Unmixing Using a Hierarchical Bayesian Model for Hyperspectral Imagery , 2008, IEEE Transactions on Signal Processing.
[12] Michael Kirby,et al. A SOLUTION PROCEDURE FOR BLIND SIGNAL SEPARATION USING THE MAXIMUM NOISE FRACTION APPROACH: ALGORITHMS AND EXAMPLES , 2001 .
[13] Hairong Qi,et al. Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization , 2007, IEEE Transactions on Geoscience and Remote Sensing.
[14] D. Stein,et al. Application of the normal compositional model to the analysis of hyperspectral imagery , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.
[15] Michael W. Berry,et al. Algorithms and applications for approximate nonnegative matrix factorization , 2007, Comput. Stat. Data Anal..
[16] Chein-I Chang,et al. Pixel purity index-based algorithms for endmember extraction from hyperspectral imagery , 2006 .
[17] Jean-Yves Tourneret,et al. Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model. Application to Hyperspectral Imagery , 2010, IEEE Transactions on Image Processing.
[18] Chein-I Chang,et al. Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[19] Qian Du,et al. Hyperspectral band selection with similarity assessment , 2009, Defense + Commercial Sensing.
[20] José M. Bioucas-Dias,et al. Does independent component analysis play a role in unmixing hyperspectral data? , 2003, IEEE Transactions on Geoscience and Remote Sensing.
[21] José M. Bioucas-Dias,et al. Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[22] Michael T. Eismann,et al. Stochastic Mixture Modeling , 2006 .
[23] L. Tierney. Markov Chains for Exploring Posterior Distributions , 1994 .
[24] Jean-Yves Tourneret,et al. Spectral Unmixing of Hyperspectral Images using a Hierarchical Bayesian Model , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[25] Andreas T. Ernst,et al. ICE: a statistical approach to identifying endmembers in hyperspectral images , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[26] Hannes Kazianka. Objective Bayesian analysis for the normal compositional model , 2012, Comput. Stat. Data Anal..
[27] D. Rubin,et al. Inference from Iterative Simulation Using Multiple Sequences , 1992 .
[28] Alfred O. Hero,et al. Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery , 2009, IEEE Transactions on Signal Processing.
[29] Raimund Leitner,et al. Spot Counting for Automated Analysis of Unmixed Hyper-Spectral M-FISH Images , 2008 .