Spectral Super-Resolution for Hyperspectral Images via Sparse Representations

The spectral dimension of hyperspectral imaging (HSI) systems plays a fundamental role in numerous terrestrial and earth observation applications, including spectral unmixing, target detection, and classification among others. However, in several cases the spectral resolution of HSI systems is sacrificed for the shake of spatial resolution, as such in the case of snapshot spectral imaging systems that acquire simultaneously the 3D datacube. We address these limitations by introducing an efficient post-acquisition spectral resolution enhancement scheme that synthesizes the full spectrum from only few acquired spectral bands. To achieve this goal we utilize a regularized sparse-based learning procedure where the relations between high and low-spectral resolution hyper-pixels are efficiently encoded via a coupled dictionary learning scheme. Experimental results and quantitative validation on data acquired by NASA’s EO-1 mission’s Hyperion sensor, demonstrate the potential of the proposed approach for accurate spectral resolution enhancement of hyperspectral imaging systems.