Han Xiaolin, Yu Jing, Xue Jing-Hao, Sun Weidong
IEEE Trans Image Process. 2020 Feb 26. doi: 10.1109/TIP.2020.2968773.
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.
光谱字典或空间字典已被广泛应用于低空间分辨率高光谱（LH）图像与高空间分辨率多光谱（HM）图像的融合。然而，仅使用光谱字典不足以保留空间信息，反之亦然。为了解决这个问题，本文提出了一种新的LH和HM图像融合方法，称为使用优化双字典的OTD。OTD的融合问题在稀疏表示框架下进行了分析性表述，作为双光谱 - 空间字典及其相应稀疏系数的优化。更具体地说，通过在光谱域中利用观测到的LH和HM图像来优化表示广义光谱的光谱字典及其光谱稀疏系数；通过对空间域中其余高频信息进行建模来优化表示空间信息的空间字典及其空间稀疏系数。此外，在没有非负约束的情况下，采用乘子交替方向法（ADMM）来实现上述优化过程。在各种数据集上与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都实现了更好的融合性能。
