proposed coupled nonnegative matrix factorization (CNMF) to estimate the endmember dictionary and abundance code from HSI and MSI, respectively. The endmember dictionary is extracted from the low-resolution HSI and the abundance code is estimated by the spatial fractional abundances of MSI. Because the MSI captures the same scene with HSI, the endmember dictionary or the abundance code should be the same. In the past several decades, a lot of research has been made to develop the efficient estimation of the endmember dictionary and the abundance code. The main limitation of these super-resolution methods is that their performance largely depends on the accuracy of estimating endmember dictionary (or endmember spectra) and abundance (or code of the dictionary). In this way, HSI super-resolution is transferred to estimate the endmember dictionary and the abundance code. The high-resolution HSI can be estimated by combining the endmember dictionary and the abundance code. The endmember dictionary denotes the “pure” spectral signatures, while the abundance code indicates the proportions of endmember spectra within each pixel. Based on the spectral mixture analysis, the original high-resolution HSI can be unmixed into endmember dictionary (or endmember spectra) and abundance code (or abundance matrix). These spectral signatures are the spectra of the underlying materials presented in the observed scene. In spectral mixture analysis, the HSI can be described by a mixture of some “pure” spectral signatures (the so-called endmembers). A general trend in the existing methods is to exploit the spatial information by spectral mixture analysis (hyperspectral unmixing). An overview of recent state-of-the-art hyperspectral and multispectral image fusion methods can be found in. Recently, many HSI super-resolution methods have been proposed to fuse low-resolution HSI with a high-resolution coincident image. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution.
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