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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Reconstruction From Multispectral to Hyperspectral Image Using Spectral Library-Based Dictionary Learning
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Reconstruction From Multispectral to Hyperspectral Image Using Spectral Library-Based Dictionary Learning

机译：基于光谱库的字典学习技术将多光谱图像重建为高光谱图像

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摘要

High-spatial hyperspectral (HH) image reconstruction using both high-spatial multispectral (HM) image and low-spatial hyperspectral (LH) image over the same scene is widely used in many real applications. Nevertheless, the pair of HM image and LH image over the same scene is hard to obtain. To solve this problem, a new HH image reconstruction method using spectral library-based dictionary learning (named as HIRSL) is proposed in this paper, only from one HM image. The above reconstruction problem is formulated in the framework of sparse representation, as an estimation of the band matching matrix, the spectral dictionary, and the sparse coefficients. More specifically, a band matching method is proposed for mapping the common spectral library to a specific spectral library corresponding to the reconstructed HH image in spectral domain. Then, an efficient spectral dictionary learning method is proposed for the construction of spectral dictionary using the matched specific spectral library, which avoids the dependence of the LH image over the same scene. Finally, the sparse coefficients of the HM image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers without nonnegative constraint. Comparison results on simulated and real data sets with the relative state-of-the-art methods demonstrate that even only using one HM image, our proposed method achieves a comparable reconstruction quality of high-spatial hyperspectral image both in spatial and spectral domains.
机译：在同一场景中同时使用高空间多光谱（HM）图像和低空间高光谱（LH）图像的高空间高光谱（HH）图像重构已广泛用于许多实际应用中。然而，难以获得同一场景上的一对HM图像和LH图像。为了解决这个问题，本文提出了一种基于光谱库的字典学习新的HH图像重建方法（称为HIRSL），仅从一个HM图像开始。上述重建问题是在稀疏表示的框架内提出的，作为对频带匹配矩阵，频谱字典和稀疏系数的估计。更具体地，提出了一种频带匹配方法，用于将公共频谱库映射到与频谱域中的重构的HH图像相对应的特定频谱库。然后，提出了一种高效的光谱字典学习方法，该方法利用匹配的特定光谱库构建光谱字典，避免了LH图像在同一场景下的依赖性。最后，在没有非负约束的情况下，使用乘法器的交替方向方法估计相对于学习的频谱字典的HM图像的稀疏系数。使用相对最新技术在模拟数据集和真实数据集上的比较结果表明，即使仅使用一个HM图像，我们提出的方法在空间和光谱域中也可以实现高空间高光谱图像的可比重建质量。

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