Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing

By Sen Jia, Y. Qian. IEEE Transactions on Geoscience and Remote Sensing, 2009

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Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization (2011, methodology): “We have illustrated the advantages of our unmixing method on synthetic and real-world data and compared our method against a number of alternatives, i.e., NMF, L 1 -NMF and L 2 -NMF, VCA, MVC-NMF, PSNMFSC and SISAL.”
Manifold Regularized Sparse NMF for Hyperspectral Unmixing (2013, background): “smoothness NMF with sparsity constraint [23] is proposed to dispose of this problem.”
The Why and How of Nonnegative Matrix Factorization (2014, background): “…or spatial coherence (neighboring pixels are more likely to contain the same materials) which are usually tackled with TV-like regularizations (that is, ℓ 1 norm of the diﬀerence between neighboring values to preserve the edges in the image); see, e.g., [68, 67], and the references therein.”