基于双线性混合模型的高光谱图像非线性光谱解混

[1] Two-Step Constrained Nonlinear Spectral Mixture Analysis Method for Mitigating the Collinearity Effect [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (05): : 2873 - 2886

[2] A Novel Approach for Efficient p-Linear Hyperspectral Unmixing [J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2015, 9 (06) : 1156 - 1168

[3] A Review of Nonlinear Hyperspectral Unmixing Methods [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (06) : 1844 - 1868

[4] A Comparison of Nonlinear Mixing Models for Vegetated Areas Using Simulated and Real Hyperspectral Data [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (06) : 1869 - 1878

[5] Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (07): : 4404 - 4423

[6] A Bilinear-Bilinear Nonnegative Matrix Factorization Method for Hyperspectral Unmixing [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2014, 11 (04) : 778 - 782

[7] Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (02): : 1430 - 1437

[8] Nonlinear Unmixing of Hyperspectral Images [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2014, 31 (01) : 82 - 94

[9] Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2012, 21 (06) : 3017 - 3025

[10] Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches[J] . José M. Bioucas-Dias,Antonio Plaza,Nicolas Dobigeon,Mario Parente,Qian Du,Paul Gader,Jocelyn Chanussot.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing . 2012 (2)

[1] Two-Step Constrained Nonlinear Spectral Mixture Analysis Method for Mitigating the Collinearity Effect [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (05): : 2873 - 2886

[2] A Novel Approach for Efficient p-Linear Hyperspectral Unmixing [J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2015, 9 (06) : 1156 - 1168

[3] A Review of Nonlinear Hyperspectral Unmixing Methods [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (06) : 1844 - 1868

[4] A Comparison of Nonlinear Mixing Models for Vegetated Areas Using Simulated and Real Hyperspectral Data [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (06) : 1869 - 1878

[5] Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (07): : 4404 - 4423

[6] A Bilinear-Bilinear Nonnegative Matrix Factorization Method for Hyperspectral Unmixing [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2014, 11 (04) : 778 - 782

[7] Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (02): : 1430 - 1437

[8] Nonlinear Unmixing of Hyperspectral Images [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2014, 31 (01) : 82 - 94

[9] Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2012, 21 (06) : 3017 - 3025

[10] Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches[J] . José M. Bioucas-Dias,Antonio Plaza,Nicolas Dobigeon,Mario Parente,Qian Du,Paul Gader,Jocelyn Chanussot.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing . 2012 (2)