As the hyperspectral images (HSIs) usually have a low spatial resolution, HSI super-resolution has recently attracted more and more attention to enhance the spatial resolution of HSIs. A common method is to fuse the low-resolution (LR) HSI with a multispectral image (MSI) whose spatial resolution is higher than the HSI. In this article, we proposed a novel adaptive nonnegative sparse representation-based model to fuse an HSI and its corresponding MSI. First, basing the linear spectral unmixing, the nonnegative structured sparse representation model estimates the sparse codes of the desired high-resolution HSI from both the LR-HSI and the MSI. Then, the adaptive sparse representation can balance the relationship between the sparsity and collaboration by generating a suitable coefficient. Finally, in order to obtain more accurate results, we alternately optimize the spectral basis and coefficients rather than keeping the spectral basis fixed. The alternating direction method of multipliers is applied to solve the proposed optimization problem. The experimental results on both ground-based HSIs and real remote sensing HSIs show the superiority of our proposed approach to some other state-of-the-art HSI super-resolution methods.

中文翻译：

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高光谱图像超分辨率的自适应非负稀疏表示


由于高光谱图像（HSI）通常具有较低的空间分辨率，HSI超分辨率最近引起了越来越多的关注，以提高HSI的空间分辨率。一种常见的方法是将低分辨率（LR）HSI与空间分辨率高于HSI的多光谱图像（MSI）融合。在本文中，我们提出了一种新颖的基于自适应非负稀疏表示的模型来融合 HSI 及其相应的 MSI。首先，基于线性谱分解，非负结构化稀疏表示模型从 LR-HSI 和 MSI 估计所需高分辨率 HSI 的稀疏代码。然后，自适应稀疏表示可以通过生成合适的系数来平衡稀疏性和协作之间的关系。最后，为了获得更准确的结果，我们交替优化谱基和系数，而不是保持谱基固定。应用乘子交替方向法来解决所提出的优化问题。地面 HSI 和真实遥感 HSI 的实验结果表明，我们提出的方法相对于其他一些最先进的 HSI 超分辨率方法具有优越性。