An initially robust minimum simplex volume-based method for linear hyperspectral unmixing

References

• Ambikapathi, A., T. H. Chan, W. K. Ma, and C. Y. Chi. 2011. “Chance-Constrained Robust Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing 49 (11): 4194–4209. https://doi.org/10.1109/TGRS.2011.2151197.
   Web of Science ®Google Scholar
• Berman, M., H. Kiiveri, R. Lagerstrom, A. Ernst, R. Dunne, and J. F. Huntington. 2004. “ICE: A Statistical Approach to Identifying Endmembers in Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 42 (10): 2085–2095. https://doi.org/10.1109/TGRS.2004.835299.
   Web of Science ®Google Scholar
• Bioucas-Dias, J. M. 2009. “A Variable Splitting Augmented Lagrangian Approach to Linear Spectral Unmixing.” In Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, France. https://doi.org/10.1109/WHISPERS.2009.5289072.
   Google Scholar
• Boardman, J., F. Kruse, and R. Green. 1995. “Mapping target signatures via partial unmixing of AVIRIS data.” The Fifth Annual JPL Airborne Earth Science Workshop, Dec. 1995, Pasadena, CA, Vol. 1, 23–26.
   Google Scholar
• Chan, T. H., C. Y. Chi, Y. M. Huang, and W. K. Ma. 2009. “A Convex Analysis-Based Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing.” IEEE Transactions on Signal Processing 57 (11): 4418–4432. https://doi.org/10.1109/TSP.2009.2025802.
   Web of Science ®Google Scholar
• Chang, C.-I., S.-Y. Chen, H.-C. Li, H.-M. Chen, and C.-H. Wen. 2016. “Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (9): 4280–4306. https://doi.org/10.1109/jstars.2016.2555960.
   Web of Science ®Google Scholar
• Chan, T.-H., W.-K. Ma, A. Ambikapathi, and C.-Y. Chi. 2011. “A Simplex Volume Maximization Framework for Hyperspectral Endmember Extraction.” IEEE Transactions on Geoscience and Remote Sensing 49 (11): 4177–4193. https://doi.org/10.1109/tgrs.2011.2141672.
   Web of Science ®Google Scholar
• Drumetz, L., M. A. Veganzones, S. Henrot, R. Phlypo, J. Chanussot, and C. Jutten. 2016. “Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability.” IEEE Transactions on Image Processing 25 (8): 3890–3905. https://doi.org/10.1109/TIP.2016.2579259.
   PubMed Web of Science ®Google Scholar
• Du, Q., N. Raksuntorn, N. H. Younan, and R. L. King. 2008. “End-Member Extraction for Hyperspectral Image Analysis.” Applied Optics 47 (28): F77–84. Du, Qian Raksuntorn, Nareenart Younan, Nicolas H King, Roger L eng 2008/10/03 Appl Opt. 2008;47(28):F77-84. do i:10.1364/ao.47.000f77. https://doi.org/10.1364/ao.47.000f77.
   PubMed Web of Science ®Google Scholar
• Heinz, D. C., and C.-I. Chang. 2002. “Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 39 (3): 529–545. https://doi.org/10.1109/36.911111.
   Web of Science ®Google Scholar
• Heylen, R., D. Burazerovic, and P. Scheunders. 2011. “Fully Constrained Least Squares Spectral Unmixing by Simplex Projection.” IEEE Transactions on Geoscience and Remote Sensing 49 (11): 4112–4122. https://doi.org/10.1109/tgrs.2011.2155070.
   Web of Science ®Google Scholar
• Kingma, D., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.” Computer Science. https://doi.org/10.48550/arXiv.1412.6980.
   Google Scholar
• Li, J., A. Agathos, D. Zaharie, J. M. Bioucas-Dias, A. Plaza, and X. Li. 2015. “Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing 53 (9): 5067–5082. https://doi.org/10.1109/tgrs.2015.2417162.
   Web of Science ®Google Scholar
• Li, J., and J. M. Bioucas-Dias. 2008. “Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data.” In IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, Vol. 3, 250–253.
   Google Scholar
• Miao, L., and H. Qi. 2007. “Endmember Extraction from Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization.” IEEE Transactions on Geoscience and Remote Sensing 45 (3): 765–777. https://doi.org/10.1109/tgrs.2006.888466.
   Web of Science ®Google Scholar
• Nascimento, J. M. P., and J. M. Bioucas-Dias. 2005. “Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 43 (4): 898–910. https://doi.org/10.1109/tgrs.2005.844293.
   Web of Science ®Google Scholar
• Packer, A. 2002. “NP-Hardness of Largest Contained and Smallest Containing Simplices for V- and H-Polytopes.” Discrete Computing Geometry 28 (3): 349–377. https://doi.org/10.1007/s00454-002-0745-8.
   Web of Science ®Google Scholar
• Plaza, A., and C.-I. Chang. 2006. “Impact of Initialization on Design of Endmember Extraction Algorithms.” IEEE Transactions on Geoscience and Remote Sensing 44 (11): 3397–3407. https://doi.org/10.1109/tgrs.2006.879538.
   Web of Science ®Google Scholar
• Rasti, B., B. Koirala, P. Scheunders, and J. Chanussot. 2022. “MiSicnet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing.” IEEE Transactions on Geoscience & Remote Sensing 60:1–15. https://doi.org/10.1109/tgrs.2022.3146904.
   Web of Science ®Google Scholar
• Wang, W., and C.-P. Miguel. 2013. “Projection Onto the Probability Simplex: An Efficient Algorithm with a Simple Proof, and an Application.” Cornell University - arXiv,Cornell University - arXiv abs:2309–2541.
   Google Scholar
• Winter, M. E. 1999. “N-FINDER: An Algorithm for Fast Autonomous Spectral End-Member Determination in Hyperspectral Data.” In Spies International Symposium on Optical Science, Denver, CO, United States, Vol. 3753, 266–275. https://doi.org/10.1117/12.366289.
   Google Scholar
• Zhuang, L., C.-H. Lin, M. A. T. Figueiredo, and J. M. Bioucas-Dias. 2019. “Regularization Parameter Selection in Minimum Volume Hyperspectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing 57 (12): 9858–9877. https://doi.org/10.1109/tgrs.2019.2929776.
   Web of Science ®Google Scholar