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Nowadays, nonnegative matrix factorization (NMF) based methods have beenwidely applied to blind spectral unmixing. Introducing proper regularizers toNMF is crucial for mathematically constraining the solutions and physicallyexploiting spectral and spatial properties of images. Generally, properlyhandcrafting regularizers and solving the associated complex optimizationproblem are non-trivial tasks. In our work, we propose an NMF based unmixingframework which jointly uses a handcrafting regularizer and a learntregularizer from data. we plug learnt priors of abundances where the associatedsubproblem can be addressed using various image denoisers, and we consider anl_2,1-norm regularizer to the abundance matrix to promote sparse unmixingresults. The proposed framework is flexible and extendable. Both synthetic dataand real airborne data are conducted to confirm the effectiveness of ourmethod.

Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors