An Abundance Characteristic-Based Independent Component Analysis for Hyperspectral Unmixing
暂无分享,去创建一个
Bo Du | Liangpei Zhang | Nan Wang | Lifu Zhang | Bo Du | Nan Wang | Lifu Zhang | Liangpei Zhang
[1] H. Sebastian Seung,et al. Algorithms for Non-negative Matrix Factorization , 2000, NIPS.
[2] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[3] G. Foody. Sub-pixel methods in remote sensing , 2004 .
[4] Antonio J. Plaza,et al. Spatial/spectral endmember extraction by multidimensional morphological operations , 2002, IEEE Trans. Geosci. Remote. Sens..
[5] Jun Zhou,et al. Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[6] José M. Bioucas-Dias,et al. Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.
[7] Victoria Stodden,et al. When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts? , 2003, NIPS.
[8] Jean-Yves Tourneret,et al. Estimating the Number of Endmembers in Hyperspectral Images Using the Normal Compositional Model and a Hierarchical Bayesian Algorithm , 2010, IEEE Journal of Selected Topics in Signal Processing.
[9] Paul D. Gader,et al. SPICE: a sparsity promoting iterated constrained endmember extraction algorithm with applications to landmine detection from hyperspectral imagery , 2007, SPIE Defense + Commercial Sensing.
[10] Shengli Xie,et al. Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization , 2011, IEEE Transactions on Image Processing.
[11] Xiaohui Zhang,et al. Independent component analysis for remote sensing study , 1999, Remote Sensing.
[12] Jean-Yves Tourneret,et al. Bayesian separation of spectral sources under non-negativity and full additivity constraints , 2009, Signal Process..
[13] Peter Bajorski,et al. Second Moment Linear Dimensionality as an Alternative to Virtual Dimensionality , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[14] José M. Bioucas-Dias,et al. Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[15] Frank Sandon,et al. The Advanced Theory of Statistics. II , 1947, The Mathematical Gazette.
[16] José M. Bioucas-Dias,et al. Does independent component analysis play a role in unmixing hyperspectral data? , 2005, IEEE Trans. Geosci. Remote. Sens..
[17] Mireille Guillaume,et al. Minimum Dispersion Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[18] Dacheng Tao,et al. Double Shrinking Sparse Dimension Reduction , 2013, IEEE Transactions on Image Processing.
[19] Marco Diani,et al. Hyperspectral Signal Subspace Identification in the Presence of Rare Signal Components , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[20] Mark D. Plumbley. Algorithms for nonnegative independent component analysis , 2003, IEEE Trans. Neural Networks.
[21] Chein-I Chang,et al. A New Growing Method for Simplex-Based Endmember Extraction Algorithm , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[22] Antonio J. Plaza,et al. A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[23] Bo Du,et al. An Endmember Dissimilarity Constrained Non-Negative Matrix Factorization Method for Hyperspectral Unmixing , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[24] Mario Winter,et al. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data , 1999, Optics & Photonics.
[25] Wei Xia,et al. Independent Component Analysis for Blind Unmixing of Hyperspectral Imagery With Additional Constraints , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[26] José M. Bioucas-Dias,et al. A variable splitting augmented Lagrangian approach to linear spectral unmixing , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.
[27] Chein-I Chang,et al. Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[28] Lifu Zhang,et al. Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[29] I. Ginsberg,et al. Unsupervised hyperspectral image analysis using independent component analysis , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).
[30] Sen Jia,et al. Spectral and Spatial Complexity-Based Hyperspectral Unmixing , 2007, IEEE Transactions on Geoscience and Remote Sensing.
[31] Wei Xia,et al. An approach based on constrained nonnegative matrix factorization to unmix hyperspectral data , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[32] 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.
[33] Alfred O. Hero,et al. Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms , 2013, IEEE Signal Processing Magazine.
[34] José M. Bioucas-Dias,et al. Hyperspectral Unmixing Based on Mixtures of Dirichlet Components , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[35] F. Sengpiel,et al. Optical imaging of intrinsic signals: recent developments in the methodology and its applications , 2004, Journal of Neuroscience Methods.
[36] Chong-Yung Chi,et al. A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing , 2009, IEEE Trans. Signal Process..
[37] Alan Edelman,et al. The Geometry of Algorithms with Orthogonality Constraints , 1998, SIAM J. Matrix Anal. Appl..
[38] Karl J. Friston. Experimental Design and Statistical Parametric Mapping , 2003 .
[39] José M. Bioucas-Dias,et al. Hyperspectral unmixing algorithm via dependent component analysis , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.
[40] Bruno A. Olshausen,et al. Learning Sparse Codes for Hyperspectral Imagery , 2011, IEEE Journal of Selected Topics in Signal Processing.
[41] Jon Atli Benediktsson,et al. On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation , 2008, Neurocomputing.
[42] J. Boardman. Automating spectral unmixing of AVIRIS data using convex geometry concepts , 1993 .
[43] S. M. Jong,et al. Remote Sensing Image Analysis: Including The Spatial Domain , 2011 .
[44] Alina Zare,et al. Spatial-spectral unmixing using fuzzy local information , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.
[45] Chein-I. Chang. Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .
[46] Robert F. Cromp,et al. Analyzing hyperspectral data with independent component analysis , 1998, Other Conferences.
[47] Antonio J. Plaza,et al. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[48] Chein-I Chang,et al. Orthogonal subspace projection (OSP) revisited: a comprehensive study and analysis , 2005, IEEE Trans. Geosci. Remote. Sens..
[49] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[50] Dewen Hu,et al. OI and fMRI Signal Separation Using Both Temporal and Spatial Autocorrelations , 2010, IEEE Transactions on Biomedical Engineering.
[51] Erkki Oja,et al. Independent Component Analysis , 2001 .
[52] A F Goetz,et al. Imaging Spectrometry for Earth Remote Sensing , 1985, Science.
[53] John F. Mustard,et al. Spectral unmixing , 2002, IEEE Signal Process. Mag..
[54] Sen Jia,et al. Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing , 2009, IEEE Transactions on Geoscience and Remote Sensing.