A hyperspectral image (HSI) contains many narrow spectral channels, thus containing efficient information in the spectral domain. However, high spectral resolution usually leads to lower spatial resolution as a result of the limitations of sensors. Hyperspectral super-resolution aims to fuse a low spatial resolution HSI with a conventional high spatial resolution image, producing an HSI with high resolution in both the spectral and spatial dimensions. In this paper, we propose a spatial group sparsity regularization unmixing-based method for hyperspectral super-resolution. The hyperspectral image (HSI) is pre-clustered using an improved Simple Linear Iterative Clustering (SLIC) superpixel algorithm to make full use of the spatial information. A robust sparse hyperspectral unmixing method is then used to unmix the input images. Then, the endmembers extracted from the HSI and the abundances extracted from the conventional image are fused. This ensures that the method makes full use of the spatial structure and the spectra of the images. The proposed method is compared with several related methods on public HSI data sets. The results demonstrate that the proposed method has superior performance when compared to the existing state-of-the-art.

中文翻译：

---

基于空间群稀疏性正则化分解的高光谱图像超分辨率

高光谱图像（HSI）包含许多狭窄的光谱通道，因此在光谱域中包含有效信息。然而，由于传感器的局限性，高光谱分辨率通常导致较低的空间分辨率。高光谱超分辨率旨在将低空间分辨率的HSI与常规的高空间分辨率的图像融合在一起，从而在光谱和空间维度上产生具有高分辨率的HSI。在本文中，我们提出了一种基于空间群稀疏性正则化分解的高光谱超分辨率方法。使用改进的简单线性迭代聚类（SLIC）超像素算法对高光谱图像（HSI）进行预聚类，以充分利用空间信息。然后使用鲁棒的稀疏高光谱分解方法来分解输入图像。然后，从HSI提取的末端成员与从常规图像提取的丰度融合在一起。这确保了该方法充分利用了图像的空间结构和光谱。将该方法与公共HSI数据集上的几种相关方法进行了比较。结果表明，与现有技术相比，该方法具有更好的性能。