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Hyperspectral unmixing (HU) is a very useful and increasingly popular preprocessing step for a wide range of hyperspectral applications.
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2011
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O. Eches, N. Dobigeon, and J.-Y. Tourneret, “Enhancing hyperspectral image unmixing with spatial correlations,”
2011
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2011
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J. Bioucas-Dias and A. Plaza, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,”
2012
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C. Li, T. Sun, K. Kelly, and Y. Zhang, “A compressive sensing and unmixing scheme for hyperspectral data processing,”
2012
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M.-D. Iordache, J. Bioucas-Dias, and A. Plaza, “Total variation spatial regularization for sparse hyperspectral unmixing,”
2012
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G. Martin and A. Plaza, “Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data,”
2012
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2015
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2015
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W. Dong, F. Fu, G. Shi, X. Cao, J. Wu, G. Li, and X. Li, “Hyperspectral image super-resolution via non-negative structured sparse representation,”
2016
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2016
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2016
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W. Wang and Y. Qian, “Parallel adaptive sparsity-constrained nmf algorithm for hyperspectral unmixing,” in
2016
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L. Tong, J. Zhou, Y. Qian, X. Bai, and Y. Gao, “Nonnegative-matrix-factorization-based hyperspectral unmixing with partially known endmembers,”
2016
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H. Li, Y. Wang, S. Xiang, J. Duan, F. Zhu, and C. Pan, “A label propagation method using spatial-spectral consistency for hyperspectral image classification,”
2016
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X. Hu, Y. Wang, F. Zhu, and C. Pan, “Learning-based fully 3d face reconstruction from a single image,” in
2016
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H. K. Aggarwal and A. Majumdar, “Hyperspectral unmixing in the presence of mixed noise using joint-sparsity and total variation,”
2016
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W. He, H. Zhang, and L. Zhang, “Sparsity-regularized robust non-negative matrix factorization for hyperspectral unmixing,”
2016
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W. Wang, Y. Qian, and Y. Y. Tang, “Hypergraph-regularized sparse nmf for hyperspectral unmixing,”
2016
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L. Zhang, W. Wei, Y. Zhang, H. Yan, F. Li, and C. Tian, “Locally similar sparsity-based hyperspectral compressive sensing using unmixing,”
2016
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J. Yao, X. Zhu, F. Zhu, and J. Huang, “Deep correlational learning for survival prediction from multi-modality datay,” in
2017
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X. Zhu, J. Yao, F. Zhu, and J. Huang, “Wsisa: Making survival prediction from whole slide histopathological images,” in
2017
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Z. Xu, S. Wang, F. Zhu, and J. Huang, “Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery,” in
2017
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F. Zhu and P. Liao, “Effective warm start for the online actor-critic reinforcement learning based mhealth intervention,” in
2017
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2017
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L. Tong, J. Zhou, X. Li, Y. Qian, and Y. Gao, “Region-based structure preserving nonnegative matrix factorization for hyperspectral unmixing,”
2017
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Y. Qian, F. Xiong, S. Zeng, J. Zhou, and Y. Y. Tang, “Matrix-vector nonnegative tensor factorization for blind unmixing of hyperspectral imagery,”
2017
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2017
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J. Sigurdsson, M. O. Ulfarsson, J. R. Sveinsson, and J. M. Bioucas-Dias, “Sparse distributed multitemporal hyperspectral unmixing,”
2017
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W. He, H. Zhang, and L. Zhang, “Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing,”
2017
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Q. Wei, M. Chen, J.-Y. Tourneret, and S. Godsill, “Unsupervised nonlinear spectral unmixing based on a multilinear mixing model,”
2017
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S. Bernabé, G. Botella, G. Martín, M. Prieto-Matias, and A. Plaza, “Parallel implementation of a full hyperspectral unmixing chain using opencl,”
2017
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X. Wang, Y. Zhong, L. Zhang, and Y. Xu, “Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing,”
2017
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E. Martel, R. Guerra, S. López, and R. Sarmiento, “A gpu-based processing chain for linearly unmixing hyperspectral images,”
2017
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W. Li, G. Wu, and Q. Du, “Transferred deep learning for anomaly detection in hyperspectral imagery,”
2017
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Q. Wang, W. Shi, P. M. Atkinson, and Q. Wei, “Approximate area-to-point regression kriging for fast hyperspectral image sharpening,”
2017
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2017
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M. Vafadar and H. Ghassemian, “Hyperspectral anomaly detection using outlier removal from collaborative representation,” in
2017
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X. Xu, X. Tong, A. Plaza, Y. Zhong, H. Xie, and L. Zhang, “Using linear spectral unmixing for subpixel mapping of hyperspectral imagery: A quantitative assessment,”
2017
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