Blind Unmixing using Dispersion Model-based Autoencoder to Address Spectral Variability

Over the past few decades, researchers have proposed various hyperspectral unmixing (HU) methods. Among these methods, deep learning (DL) has emerged as a promising approach for HU, providing new opportunities for advancement. However, accurately quantifying the presence of spectral variability factors within a mixture remains a challenging task. Therefore, numerous literatures have concerned the HU with spectral variability, in which the variation spectra are generated through the network. However, there is a lack of the connection between the network and spectral variability, so they fail to provide physically meaningful interpretability of spectral variability. To this end, we use physics-driven model to represent spectral variability and introduce it to the two-stream autoencoder unmixing network, resulting in the improved endmember and abundance estimations. Specifically, the endmember extraction network learn spectral variability parameters associated the dispersion model to generate the variations of spectra, which enhancing physical interpretability of endmember variability. In addition, the abundance estimation autoencoder network, tied to the endmember extraction network by shared weights, estimates abundances using the reconstructed hyperspectral image. Compared with the state-of-the-art HU approaches on three real hyperspectral image datasets, our method outperforms these techniques with improved unmixing accuracy, especially on endmember estimation.