Unmixing Autoencoder for Image Reconstruction from Hyperspectral Data

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.