Spectral Super-Resolution Based on Dictionary Optimization Learning via Spectral Library
Hao-Fang Yan, Yongqiang Zhao, Jonathan Cheung-Wai Chan, Seong G. Kong
Abstract
Extensive works have been reported in hyperspectral images (HSIs) and multispectral images (MSIs) fusion to raise the spatial resolution of HSIs. However, the limited acquisition of HSIs has been an obstacle to such approaches. Spectral super-resolution (SSR) of MSI is a challenging and less investigated topic, which can also provide high-resolution synthetic HSIs. To deal with this high ill-posedness problem, we perform super-resolution enhancement of MSIs in the spectral domain by incorporating a spectral library as a priori. First, an aligned spectral library, which maps the open-source spectral library to a specific spectral library created for the reconstructed HR HSI, is represented. An intermediate latent HSI is obtained by fusing the spatial information from MSI and the hyperspectral information from a specific spectral library. Then, we use low-rank attribute embedding to transfer latent HSI into a robust subspace. Finally, a low-rank HSI dictionary representing the hyperspectral information is learned from the latent HSI. The adaptive sparse coefficient of MSI is obtained with a nonnegative constraint. By fusing these two terms, we get the final HR HSI. The proposed SSR model does not require any pretraining stages. We confirm the validity and superiority of our proposed SSR algorithm by comparing it with several benchmark state-of-the-art approaches on different datasets.