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A graph Laplacian regularization for hyperspectral data unmixing
[article]
2014
arXiv
pre-print
This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation. The proposed regularization relies upon the construction of a graph representation of the hyperspectral image. Each node in the graph represents a pixel's spectrum, and edges connect spectrally and spatially similar pixels. The proposed graph framework promotes smoothness in the estimated abundance maps and collaborative estimation between homogeneous areas of the image. The resulting convex
arXiv:1410.3699v1
fatcat:gketmi4oprfefmi6duci6itlym