Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ineffective. Although feature extraction methods have been applied, the chemical meaning of the compressed variables is difficult to interpret, limiting their further applications. An unmixing autoencoder (UAE) was developed in this work for the separation of the mixed spectra in HSI. The proposed model is composed of an encoder and a fully connected (FC) layer. The former is used to compress the input spectrum into several variables, and the latter is employed to reconstruct the spectrum. Combining reconstruction loss and sparse regularization, the weights and the spectral profiles of the components will be encoded in the compressed variables and the connection weights of FC, respectively. A simulated and three experimental HSI data sets were adopted to investigate the performance of the UAE model. The spectral components were successfully obtained, from which the handwriting under papers was revealed from the image of near-infrared (NIR) diffusive reflectance spectroscopy, and the images of lipids, proteins, and nucleic acids were reconstructed from the Raman and stimulated Raman scattering (SRS) images.

中文翻译：

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从高光谱数据中解混用于图像重建的自动编码器


由于样品的复杂性和空间分辨率的限制，高光谱成像 （HSI） 中的光谱通常由多个分量贡献，这使得单变量分析无效。尽管已经应用了特征提取方法，但压缩变量的化学含义难以解释，限制了它们的进一步应用。在这项工作中开发了一种解混自动编码器 （UAE），用于分离 HSI 中的混合光谱。所提出的模型由一个编码器和一个全连接 （FC） 层组成。前者用于将输入频谱压缩为多个变量，后者用于重建频谱。结合重建损耗和稀疏正则则化，分量的权重和光谱分布将分别编码在 FC 的压缩变量和连接权重中。采用一个模拟和三个实验 HSI 数据集来研究 UAE 模型的性能。成功获得光谱分量，从近红外 （NIR） 漫反射光谱图像中揭示纸张下的笔迹，并从拉曼和受激拉曼散射 （SRS） 图像中重建脂质、蛋白质和核酸的图像。