Convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing

By Tsung-Han Chan, Chong-Yung Chi, Yu-Min Huang, Wing-Kin Ma. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009

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Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization (2011, background): “au) minimum volume based algorithms [6], [7], [8], [9], [10] and flexible similarity measures [11].”
A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing (2014, background): “M Also, { , , } b b conv N 1 f is a full-dimensional simplex [36].”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, positive): “Although the optimization problem (10) is nonconvex, it is proved in (10) that the existence of pure pixels is a sufÞcient condition for MVES to identify the true endmembers.”
A variable splitting augmented Lagrangian approach to linear spectral unmixing (2009, methodology): “Relevant works exploiting this direction are the minimum volume enclosing simplex (MYES) [14], the minimum volume simplex analysys (MYSA) [15], and the nonnegative matrix factorization minimum volume transform (NMF-MYT) [16].”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “The minimum volume estimation (MVES) algorithm [147] integrates concepts of convex analysis and volume minimization to provide a solution similar to that of previously mentioned algorithms but using cyclic minimization with linear programming.”