Collaborative Sparse Regression for Hyperspectral Unmixing

By Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio J. Plaza. IEEE Transactions on Geoscience and Remote Sensing, 2014

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Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “This allows the number of endmembers participating in the final solution to be minimized [164], while also partially circumventing the need to estimate the number of endmembers in the scene [165].”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “The image on the right hand side is the solution of the group sparse problem using the mixed , 2 1 , norm and was computed with the CLSUnSAL algorithm introduced in [74].”
A Review of Nonlinear Hyperspectral Unmixing Methods (2014, background): “The researcher might also try to avoid mixtures with a high number of endmember contributions since they might be unrealistic in certain scenarios, or one might want to promote the use of the same set of library endmembers for the entire image [192].”
A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing (2014, methodology): “An efficient solver for (38) is provided in [54], where a divide-and-conquer optimization strategy, specifically, the alternating direction method of multipliers (ADMM), was implemented.”