Spectral Super-Resolution for Multispectral Image Based on Spectral Improvement Strategy and Spatial Preservation Strategy

While hyperspectral (HS) images play a significant role in many applications, they often suffer from issues such as low spatial resolution, low temporal resolution, and some of the acquired spectral bands are either with low signal-to-noise ratio (SNR) or invalid because of the very high-noise level. To address this issue, a spectral super-resolution method is proposed in this paper to recover a high-spectral-resolution HS image from multispectral (MS) images. The reconstructed HS image will have the same spatial resolution and coverage as the input MS image. 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 an HS image is obtained as an intermediate result. Then, spectral dictionary learning is exploited to recover a more accurate spectral reconstruction result. Spatial preservation strategy is used as a spatial constraint to ensure spatial consistency. In addition, the low-rank property of HS image is also introduced to make the use of global spectral coherence among HS bands. Experiments are conducted on both simulated and real datasets including spectral enhancement of RGB image and the MS image generated by AVIRIS data and real MS/HS data (ALI and Hyperion) captured by Earth Observing-1 (EO-1) satellite. Experiment results demonstrate the superiority of our proposed method to other state-of-the-art methods.