Sparse coding with morphology segmentation and multi-label fusion for hyperspectral image super-resolution

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Hyperspectral image (HSI) super-solution to reconstruct high spatial resolution HSIs has attracted increasing interest in recent years. In this paper, we propose a HSI super-resolution framework based on sparse coding with morphology segmentation and multi-label fusion (MSML), which is composed of four stages: (1) A spectral dictionary is learned by the online dictionary learning approach from the given HSI; (2) The morphology segmentation technique is introduced to divide each multispectral image (MSI) band into a series of regions associated with a label map; (3) A weighted voting based multi-label fusion model is constructed to combine multiple label maps from MSI bands to determine 3-D patches; (4) A sparse coding model is built to calculate sparse coefficients of 3-D patches that are used for the HSI super-solution. Compared with traditional sparse representation based algorithms, the novel MSML method can more fully utilize the local spatial information of the MSI to realize the super-resolution, relying on the sparse coding on unfixed-size patches adaptively obtained by the morphology segmentation and multi-label fusion. The Indian Pines, Salinas, Botswana, and Pavia University datasets are used to evaluate the performance of our method. Experimental results indicate that the MSML achieves better super-resolution performance in contrast to state-of-the-art algorithms.