Spectral Library-Based Spectral Super-Resolution Under Incomplete Spectral Coverage Conditions

Spectral library-based spectral super-resolution is an effective but challenging way to obtain high-spatial hyperspectral images (HSIs) from high-spatial multispectral images (MSIs). However, the incomplete spectral coverage of spectral response functions (SRFs) makes it impossible to comprehensively sense the spectral information in the imaging model, thus greatly limits the performance of spectral super-resolution. To deal with this problem, a new spectral library-based spectral super-resolution method under incomplete spectral coverage conditions is proposed in this article. More specifically, a strategy for acquiring a typical set of spectra from the spectral library is proposed, trying to provide spectral observations under incomplete spectral coverage conditions. Second, taking the typical set of spectra and the remaining spectral library as a priori, a new spectral super-resolution model is established under sparse and low-rank constraints. And then, the spectral dictionary is optimized utilizing the spectral information supplied by the prior spectral library. Finally, its corresponding coefficient matrix is optimized using the spatial information supplied by the MSI and the spectral similarity constraint on the typical spectra. Experimental results using different datasets with different SRFs show that our proposed method outperforms other relative state-of-the-art methods in terms of both spectral reconstruction and spatial preservations.