Hyper-spectral Image Super-resolution Using Non-negative Spectral Representation with Data-Guided Sparsity

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Hyperspectral imaging has great potential for understanding the characteristics of different materials in many applications ranging from remote sensing to medical imaging. However, due to various hardware limitations, only low-resolution hyperspectral and high-resolution multi-spectral images can be available using existing imaging techniques. This study aims to generate a high-resolution hyperspectral image via fusion of the available LR-HS and HR-MS images. We propose a novel hyperspectral image superresolution method via non-negative sparse representation of reflectance spectral with adaptive sparsity constraint. By analyzing local content similarity of a focused pixel in the available high-resolution multi-spectral image, which can measure pixel material purity according to surrounding pixels, we generate a sparsity map for guiding non-negative sparse coding optimization procedure of the spectral representation called non-negative spectral representation with data-guided sparsity. Since the proposed method adaptively adjust the sparsity in the spectral representation based on the local content of the available high-resolution multi-spectral image, it can produce more robust spectral representation for recovering the target high-resolution hyper-spectral image. Comprehensive experiments on two public hyperspectral datasets validate that the proposed method achieves promising performances compared with the existing state of the art methods.