Multiple Clustering Guided Nonnegative Matrix Factorization for Hyperspectral Unmixing
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[1] Liangpei Zhang,et al. Unsupervised classification strategy utilizing an endmember extraction technique for airborne hyperspectral remotely sensed imagery , 2014 .
[2] Yuntao Qian,et al. Adaptive ${L}_{\bf 1/2}$ Sparsity-Constrained NMF With Half-Thresholding Algorithm for Hyperspectral Unmixing , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[3] Yuan Yan Tang,et al. Hypergraph-Regularized Sparse NMF for Hyperspectral Unmixing , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[4] Jaegul Choo,et al. Weakly supervised nonnegative matrix factorization for user-driven clustering , 2014, Data Mining and Knowledge Discovery.
[5] Yu-Jin Zhang,et al. Nonnegative Matrix Factorization: A Comprehensive Review , 2013, IEEE Transactions on Knowledge and Data Engineering.
[6] Gang Hua,et al. Hyperspectral Image Classification Through Bilayer Graph-Based Learning , 2014, IEEE Transactions on Image Processing.
[7] Zhigang Luo,et al. NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization , 2012, IEEE Transactions on Signal Processing.
[8] Liang-pei Zhang,et al. A Poisson nonnegative matrix factorization method with parameter subspace clustering constraint for endmember extraction in hyperspectral imagery , 2017 .
[9] Xuelong Li,et al. Structurally Incoherent Low-Rank Nonnegative Matrix Factorization for Image Classification , 2018, IEEE Transactions on Image Processing.
[10] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[11] Heng Tao Shen,et al. Semi-Paired Discrete Hashing: Learning Latent Hash Codes for Semi-Paired Cross-View Retrieval , 2017, IEEE Transactions on Cybernetics.
[12] H. Sebastian Seung,et al. Algorithms for Non-negative Matrix Factorization , 2000, NIPS.
[13] Jun Zhou,et al. Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[14] José M. Bioucas-Dias,et al. Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.
[15] Johannes R. Sveinsson,et al. Semi-supervised hyperspectral unmixing , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.
[16] Antonio J. Plaza,et al. Sparse Unmixing of Hyperspectral Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[17] Chih-Jen Lin,et al. Projected Gradient Methods for Nonnegative Matrix Factorization , 2007, Neural Computation.
[18] Wei Xia,et al. An approach based on constrained nonnegative matrix factorization to unmix hyperspectral data , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[19] Bing Zhang,et al. Self-Supervised Low-Rank Representation (SSLRR) for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[20] Melba M. Crawford,et al. Unsupervised multistage image classification using hierarchical clustering with a bayesian similarity measure , 2005, IEEE Transactions on Image Processing.
[21] Chein-I Chang,et al. Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[22] Xuelong Li,et al. Manifold Regularized Sparse NMF for Hyperspectral Unmixing , 2013, IEEE Transactions on Geoscience and Remote Sensing.
[23] Liangpei Zhang,et al. Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[24] Qingquan Li,et al. Three-Dimensional Local Binary Patterns for Hyperspectral Imagery Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[25] Mario Winter,et al. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data , 1999, Optics & Photonics.
[26] Meng Wang,et al. Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder , 2018, IEEE Transactions on Image Processing.
[27] J. Boardman. Automating spectral unmixing of AVIRIS data using convex geometry concepts , 1993 .
[28] Hyunsoo Kim,et al. Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method , 2008, SIAM J. Matrix Anal. Appl..
[29] Xiaoqiang Lu,et al. Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[30] Hao Wu,et al. Double Constrained NMF for Hyperspectral Unmixing , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[31] Weiwei Liu,et al. Multilabel Prediction via Cross-View Search , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[32] 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.
[33] Qian Du,et al. Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[34] S. J. Sutley,et al. Ground-truthing AVIRIS mineral mapping at Cuprite, Nevada , 1992 .
[35] 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.
[36] 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..
[37] Scott Rickard,et al. Blind separation of speech mixtures via time-frequency masking , 2004, IEEE Transactions on Signal Processing.
[38] Alexander Wong,et al. K-P-Means: A Clustering Algorithm of K “Purified” Means for Hyperspectral Endmember Estimation , 2014, IEEE Geoscience and Remote Sensing Letters.
[39] Ricardo Augusto Borsoi,et al. Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[40] Yuan Yan Tang,et al. Hyperspectral Unmixing via Total Variation Regularized Nonnegative Tensor Factorization , 2019, IEEE Transactions on Geoscience and Remote Sensing.
[41] Yong Xiang,et al. Adaptive Method for Nonsmooth Nonnegative Matrix Factorization , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[42] Alfred O. Hero,et al. Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery , 2009, IEEE Transactions on Signal Processing.
[43] Michael W. Berry,et al. Algorithms and applications for approximate nonnegative matrix factorization , 2007, Comput. Stat. Data Anal..
[44] José M. Bioucas-Dias,et al. Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[45] Antonio J. Plaza,et al. Commodity cluster-based parallel processing of hyperspectral imagery , 2006, J. Parallel Distributed Comput..
[46] Xiaojun Wu,et al. Graph Regularized Nonnegative Matrix Factorization for Data Representation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[47] Jinfei Wang,et al. Constrained Nonnegative Tensor Factorization for Spectral Unmixing of Hyperspectral Images: A Case Study of Urban Impervious Surface Extraction , 2019, IEEE Geoscience and Remote Sensing Letters.
[48] Abhinav Gupta,et al. Transitive Invariance for Self-Supervised Visual Representation Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[49] Alfonso Fernández-Manso,et al. Spectral unmixing , 2012 .
[50] Maxim Shoshany,et al. Spatially adaptive hyperspectral unmixing through endmembers analytical localization based on sums of anisotropic 2D Gaussians , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.
[51] Jun Zhou,et al. Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[52] Liangpei Zhang,et al. Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[53] Nan Wang,et al. A non-negative matrix factorization approach for hyperspectral unmixing with partial known endmembers , 2016, 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).
[54] Mark D. Plumbley,et al. Theorems on Positive Data: On the Uniqueness of NMF , 2008, Comput. Intell. Neurosci..
[55] Zhenwei Shi,et al. Nonnegative matrix factorization for hyperspectral unmixing using prior knowledge of spectral signatures , 2012 .
[56] Yuan Yan Tang,et al. Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[57] Wei Liu,et al. Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[58] David A. Clausi,et al. Extraction of Endmembers From Hyperspectral Images Using A Weighted Fuzzy Purified-Means Clustering Model , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[59] Sen Jia,et al. Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing , 2009, IEEE Transactions on Geoscience and Remote Sensing.