Spectral library-based spectral super-resolution is an effective but challenging way to obtain high-spatial hyperspectral images (HSIs) from high-spatial multispectral images (MSIs). However, the incomplete spectral coverage of spectral response functions (SRFs) makes it impossible to comprehensively sense the spectral information in the imaging model, thus greatly limits the performance of spectral super-resolution. To deal with this problem, a new spectral library-based spectral super-resolution method under incomplete spectral coverage conditions is proposed in this article. More specifically, a strategy for acquiring a typical set of spectra from the spectral library is proposed, trying to provide spectral observations under incomplete spectral coverage conditions. Second, taking the typical set of spectra and the remaining spectral library as a priori, a new spectral super-resolution model is established under sparse and low-rank constraints. And then, the spectral dictionary is optimized utilizing the spectral information supplied by the prior spectral library. Finally, its corresponding coefficient matrix is optimized using the spatial information supplied by the MSI and the spectral similarity constraint on the typical spectra. Experimental results using different datasets with different SRFs show that our proposed method outperforms other relative state-of-the-art methods in terms of both spectral reconstruction and spatial preservations.

中文翻译：

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不完全光谱覆盖条件下基于谱库的光谱超分辨率

基于光谱库的光谱超分辨率是从高空间多光谱图像（MSI）获取高空间高光谱图像（HSIs）的有效但具有挑战性的方法。然而，光谱响应函数（SRF）的不完整光谱覆盖导致无法全面感知成像模型中的光谱信息，从而极大地限制了光谱超分辨率的性能。针对这一问题，本文提出了一种新的基于光谱库的不完全光谱覆盖条件下的光谱超分辨率方法。更具体地说，提出了一种从光谱库中获取典型光谱集的策略，试图在不完整的光谱覆盖条件下提供光谱观测。其次，以典型光谱集和剩余光谱库为先验，在稀疏和低秩约束下建立新的光谱超分辨率模型。然后，利用现有光谱库提供的光谱信息来优化光谱字典。最后，利用MSI提供的空间信息和典型光谱的光谱相似度约束来优化其相应的系数矩阵。使用具有不同 SRF 的不同数据集的实验结果表明，我们提出的方法在光谱重建和空间保留方面优于其他相对最先进的方法。