Cycle-Consistent Sparse Unmixing Network Based on Deep Image Prior

A cycle-consistent sparse unmixing network based on deep image prior (C
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SU-DIP) is proposed in this paper, to reduce the complexity of sparse unmixing (SU) algorithm and the loss of details in hyperspectral images (HSIs) simultaneously. In the proposed C
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SU-DIP network, the complex design of regularization terms in sparse unmixing is avoided, meanwhile, details of abundances are effectively retained. It employs DIP-based sparse unmixing network as the backbone, and the learning process of the network replaces the regularization term design. Furthermore, cycle consistency is introduced by cascading two backbone networks, and a cycle consistency constrained loss function is designed for image detail preservation. Experimental results illustrate that the newly proposed C
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SU-DIP network is capable of obtaining competitive unmixing results compared with several representative spectral unmixing methods.