Hyperspectral image super-resolution using deep convolutional neural network

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Limited by the existed imagery hardware, it is challenging to obtain a hyperspectral image (HSI) with a high spatial resolution. Super-resolution (SR) focuses on the ways to enhance the spatial resolution. HSI SR is a highly attractive topic in computer vision and has attracted the attention from many researchers. However, most HSI SR methods improve the spatial resolution with the important spectral information severely distorted. This paper presents an HSI SR method by combining a spatial constraint (SCT) strategy with a deep spectral difference convolutional neural network (SDCNN) model. It super-resolves the HSI while preserving the spectral information. The SCT strategy constrains the low-resolution (LR) HSI generated by the reconstructed high-resolution (HR) HSI spatially close to the input LR HSI. The SDCNN model is proposed to learn an end-to-end spectral difference mapping between the LR HSI and HR HSI. Experiments have been conducted on three databases with both indoor and outdoor scenes. Comparative analyses validate that the proposed method enhances the spatial information better than the state-of-arts methods, with spectral information preserving simultaneously.