Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach
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Wing-Kin Ma | Xiao Fu | Charilaos I. Kanatsoulis | Nicholas D. Sidiropoulos | N. Sidiropoulos | Wing-Kin Ma | Xiao Fu
[1] S. Macenka,et al. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1988 .
[2] R.G. Baraniuk,et al. Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.
[3] S. J. Sutley,et al. Ground-truthing AVIRIS mineral mapping at Cuprite, Nevada , 1992 .
[4] Chong-Yung Chi,et al. A Simplex Volume Maximization Framework for Hyperspectral Endmember Extraction , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[5] Pol Coppin,et al. Endmember variability in Spectral Mixture Analysis: A review , 2011 .
[6] José M. Bioucas-Dias,et al. Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[7] Bruno Aiazzi,et al. Improving Component Substitution Pansharpening Through Multivariate Regression of MS $+$Pan Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.
[8] Bo Yang,et al. Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clustering , 2016, IEEE Transactions on Signal Processing.
[9] Jean-Yves Tourneret,et al. Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[10] Gary A. Shaw,et al. Spectral Imaging for Remote Sensing , 2003 .
[11] R. Doerffer,et al. ROSIS imaging spectrometer and its potential for ocean parameter measurements (airborne and space-borne) , 1991 .
[12] Antonio J. Plaza,et al. A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing , 2014, IEEE Signal Processing Magazine.
[13] Nikos D. Sidiropoulos,et al. Hyperspectral Super-Resolution Via Coupled Tensor Factorization: Identifiability and Algorithms , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[14] Nikos D. Sidiropoulos,et al. Almost sure identifiability of multidimensional harmonic retrieval , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).
[15] Jocelyn Chanussot,et al. A Critical Comparison Among Pansharpening Algorithms , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[16] F. Jones. Lebesgue Integration on Euclidean Space , 1993 .
[17] Simon J. Godsill,et al. Multiband Image Fusion Based on Spectral Unmixing , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[18] Naoto Yokoya,et al. Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature , 2017, IEEE Geoscience and Remote Sensing Magazine.
[19] L. Alparone,et al. An MTF-based spectral distortion minimizing model for pan-sharpening of very high resolution multispectral images of urban areas , 2003, 2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas.
[20] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..
[21] Lieven De Lathauwer,et al. Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank- (Lr, n, Lr, n, 1) Terms - Part II: Algorithms , 2015, SIAM J. Matrix Anal. Appl..
[22] Nikos D. Sidiropoulos,et al. Non-Negative Matrix Factorization Revisited: Uniqueness and Algorithm for Symmetric Decomposition , 2014, IEEE Transactions on Signal Processing.
[23] W. J. Carper,et al. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data , 1990 .
[24] Yi Ma,et al. A non-negative sparse promoting algorithm for high resolution hyperspectral imaging , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[25] Lucien Wald,et al. Data Fusion. Definitions and Architectures - Fusion of Images of Different Spatial Resolutions , 2002 .
[26] Menas Kafatos,et al. Wavelet-based hyperspectral and multispectral image fusion , 2001, SPIE Defense + Commercial Sensing.
[27] Naoto Yokoya,et al. Hyperspectral Pansharpening: A Review , 2015, IEEE Geoscience and Remote Sensing Magazine.
[28] Jocelyn Chanussot,et al. A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[29] 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.
[30] Christine Pohl,et al. Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .
[31] Pierre Comon,et al. Exploring Multimodal Data Fusion Through Joint Decompositions with Flexible Couplings , 2015, IEEE Transactions on Signal Processing.
[32] Massimo Fornasier,et al. Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.
[33] J. G. Liu,et al. Smoothing Filter-based Intensity Modulation : a spectral preserve image fusion technique for improving spatial details , 2001 .
[34] Zhi-Quan Luo,et al. A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization , 2012, SIAM J. Optim..
[35] B. S. Manjunath,et al. Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..
[36] Mingyi He,et al. Multi-spectral and hyperspectral image fusion using 3-D wavelet transform , 2007 .
[37] Nikos D. Sidiropoulos,et al. Tensor Decomposition for Signal Processing and Machine Learning , 2016, IEEE Transactions on Signal Processing.
[38] Yuan Yan Tang,et al. Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[39] Bruno Aiazzi,et al. Hyper-Sharpening: A First Approach on SIM-GA Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[40] Giorgio Ottaviani,et al. On Generic Identifiability of 3-Tensors of Small Rank , 2011, SIAM J. Matrix Anal. Appl..
[41] Alfred O. Hero,et al. Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms , 2013, IEEE Signal Processing Magazine.
[42] Naoto Yokoya,et al. Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[43] José M. Bioucas-Dias,et al. Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.
[44] Xiao Fu,et al. On Identifiability of Nonnegative Matrix Factorization , 2017, IEEE Signal Processing Letters.
[45] Shutao Li,et al. Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Naoto Yokoya,et al. Cross-Calibration for Data Fusion of EO-1/Hyperion and Terra/ASTER , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[47] Wing-Kin Ma,et al. Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications , 2018, IEEE Signal Processing Magazine.
[48] Richard H. Bartels,et al. Algorithm 432 [C2]: Solution of the matrix equation AX + XB = C [F4] , 1972, Commun. ACM.
[49] Jean-Yves Tourneret,et al. Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation , 2015, IEEE Transactions on Image Processing.
[50] G. Golub,et al. A Hessenberg-Schur method for the problem AX + XB= C , 1979 .
[51] Brian L. Markham,et al. The operational land imager: spectral response and spectral uniformity , 2011, Optical Engineering + Applications.
[52] Naoto Yokoya,et al. Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data , 2014, IEEE Transactions on Image Processing.
[53] L. Wald,et al. Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images , 1997 .
[54] Konrad Schindler,et al. Hyperspectral Super-Resolution by Coupled Spectral Unmixing , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).