Spectral or spatial dictionary has been widely used in fusing low-spatial-resolution hyperspectral (LH) images and high-spatial-resolution multispectral (HM) images. However, only using spectral dictionary is insufficient for preserving spatial information, and vice versa. To address this problem, a new LH and HM image fusion method termed OTD using optimized twin dictionaries is proposed in this paper. The fusion problem of OTD is formulated analytically in the framework of sparse representation, as an optimization of twin spectral-spatial dictionaries and their corresponding sparse coefficients. More specifically, the spectral dictionary representing the generalized spectrums and its spectral sparse coefficients are optimized by utilizing the observed LH and HM images in the spectral domain; and the spatial dictionary representing the spatial information and its spatial sparse coefficients are optimized by modeling the rest of high-frequency information in the spatial domain. In addition, without non-negative constraints, the alternating direction methods of multipliers (ADMM) are employed to implement the above optimization process. Comparison results with the related state-of-the-art fusion methods on various datasets demonstrate that our proposed OTD method achieves a better fusion performance in both spatial and spectral domains.

中文翻译：

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使用优化双字典的高光谱和多光谱图像融合。

光谱或空间字典已广泛用于融合低空间分辨率的高光谱（LH）图像和高空间分辨率的多光谱（HM）图像。但是，仅使用频谱字典不足以保存空间信息，反之亦然。为了解决这个问题，本文提出了一种使用优化的双字典的称为OTD的LH和HM图像融合新方法。OTD的融合问题是在稀疏表示的框架内分析性地提出的，它是对双谱空间字典及其对应的稀疏系数的优化。更具体地说，通过利用在光谱域中观察到的LH和HM图像来优化代表广义光谱的光谱字典及其光谱稀疏系数。通过对空间域中的其余高频信息进行建模，优化了表示空间信息及其空间稀疏系数的空间字典。另外，在没有非负约束的情况下，采用乘法器的交替方向方法（ADMM）来实现上述优化过程。与各种数据集上相关的最新融合方法的比较结果表明，我们提出的OTD方法在空间和光谱域中均具有更好的融合性能。