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Received：21 January 2021，
Published：07 November 2023
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光谱库优化学习是将光谱库中的光谱数据作为训练样本，在严格理论推导下构建字典优化学习过程。基于光谱库优化学习，本研究提出了一种光谱超分辨率重建方法，该方法在稀疏表示框架下，通过波段匹配，将光谱库映射为与待重建高光谱图像波段相对应的特定光谱库；并利用映射后的特定光谱库与高分多光谱图像，从理论上推导、并构建基于ADMM算法的光谱字典与稀疏系数优化学习过程。多种数据集上的对比分析表明，即使仅使用一幅高分多光谱图像，本研究方法仍能恢复重建出高质量的高分高光谱图像，同时光谱超分辨率重建后的高分高光谱图像可显著提升地物分类精度。结果表明，本研究实现了仅由一幅高分多光谱图像到高分高光谱图像的高质量光谱超分辨率重建。
The spectral library can contain the spectral information on the whole types of ground surface objects in the observation area of hyperspectral images. Thus
the optimized dictionary learning via spectral library refers to the process of constructing optimized spectral dictionary under strict theoretical derivation
in which the spectra in the spectral library are used as training samples. The abovementioned process enables the spectra in the hyperspectral image to be sparsely represented under the learned spectral dictionary. To this end
a new spectral super-resolution method using optimized dictionary learning via spectral library is proposed in this study. This method uses only one high spatial multispectral image to reconstruct high spatial hyperspectral image. The aforementioned problem is formulated in the framework of sparse representation
as an estimation of the band matching matrix
the optimized spectral dictionary
and the corresponding sparse coefficients. Specifically
a band matching method is proposed to map the common spectral library to a specific spectral library corresponding to the reconstructed high spatial hyperspectral image. Then
an optimization of spectral dictionary and its corresponding sparse coefficients is derived theoretically using the alternating direction method of multipliers (ADMM) algorithm and by utilizing the abovementioned specific spectral library and the high spatial multispectral image. Comparison results with the relative methods demonstrate that our method not only can achieve a high-quality reconstruction of the high spatial hyperspectral image but also can significantly improve the classification accuracy of multispectral images by even only using one high spatial multispectral image.
We aim to reconstruct high spatial hyperspectral image only from one high spatial multispectral image with high quality.
Three steps of our proposed method are discussed in detail. First
the band matching matrix is estimated using the band wavelength information. Second
the matched spectral dictionary is optimized using the matched spectral library and the high spatial multispectral image. Third
the equivalent sparse coefficient matrix with respect to the matched spectral dictionary is derived theoretically and estimated iteratively.
Extensive experiments and comparative analyses of the proposed method are conducted on various datasets to demonstrate the performance and practical application value of our proposed method. The improvement in classification accuracy on the reconstructed high spatial hyperspectral images is also evaluated using some typical classification methods.
A spectral super-resolution method is proposed
and it uses only one high spatial multispectral image to reconstruct high spatial hyperspectral image. A band matching matrix
which is used to map the common spectral library to a specific spectral library
is obtained by solving the minimum distance problem. A spectral dictionary and its corresponding sparse coefficient matrix are optimized from the matched spectral library and the high spatial hyperspectral image by minimizing augmented Lagrangian function using ADMM iteratively. Experiments on simulated and real datasets demonstrate that our proposed method can produce comparable results for the spectral super-resolution to the other relative state-of-the-art reconstruction or fusion-based methods using additional low spatial hyperspectral image. It can also provide higher reconstruction quality than the HIRSL method without optimization. Our proposed SODL method that uses only one multispectral image may help develop new light and small high spatial hyperspectral imaging equipment.
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