Montag, M.J.M.J.MontagStephani, H.H.Stephani2022-03-052022-03-052016https://publica.fraunhofer.de/handle/publica/24647810.3390/jimaging2010007In hyperspectral images, once the pure spectra of the materials are known, hyperspectral unmixing seeks to find their relative abundances throughout the scene. We present a novel variational model for hyperspectral unmixing from incomplete noisy data, which combines a spatial regularity prior with the knowledge of the pure spectra. The material abundances are found by minimizing the resulting convex functional with a primal dual algorithm. This extends least squares unmixing to the case of incomplete data, by using total variation regularization and masking of unknown data. Numerical tests with artificial and real-world data demonstrate that our method successfully recovers the true mixture coefficients from heavily-corrupted data.enjournal article