Spectral Super-Resolution for Multispectral Image Based on Spectral and Spatial Strategies

A spectral super-resolution method is proposed in this paper to recover a high spectral resolution hyperspectral (HS) image from multispectral (MS) images. The proposed method involves spectral improvement strategy and spatial preservation strategy. For spectral improvement strategy, auxiliary MS/HS image pairs of different landscapes are exploited to estimate spectral response relationship so that a HS image is obtained as an intermediate result. Then, spectral dictionary learning is exploited to recover more accurate spectral reconstruction result. Spatial preservation strategy is used as spatial constraint to ensure spatial consistency. Additionally, low rank property of HS image is also introduced to make use of global spectral coherence among HS bands. Experiments are conducted on real MS/HS data (ALI and Hyperion) captured by EO-1 satellite. Experiment results demonstrate the superiority of our proposed method to other state-of-art methods.