Two-Stream Autoencoder-Based Hyperspectral Unmixing using Hapke Model

In recent years, due to the rapid development of deep learning, autoencoders (AEs) has become a popular technique for hyperspectral unmixing. However, most of the AE-based unmixing networks only use one network to learn the features, resulting in the imbalance of the endmembers and abundances. Furthermore, the spectral fidelity of the loss function is usually measured in a linear manner by omitting the non-linearity in spectral mixing. Therefore, we propose a two-stream autoencoder blind unmixing network combined with the non-linear Hapke unmixing model. Specifically, we construct a network for solving endmembers then transfer the estimated weight matrices to another designed abundance network. Notably, we introduce a new loss function consisting of three parts: network error estimated using spectral angle distance, reconstruction error based on the non-linear Hapke model, and TV regularization of the endmembers. Experimental results show that our method has certain advantages in endmember extraction and abundance estimation compared to other excellent unmixing methods.