WU-Net: A Weakly-Supervised Unmixing Network for Remotely Sensed Hyperspectral Imagery

Recently, enormous efforts have been made to improve the performance of the linear or nonlinear mixing model for hyperspectral unmixing, yet their ability to handle spectral variability and extract physically meaningful endmembers remains limited. Based on the powerful learning ability of deep learning, we propose a weakly-supervised unmixing network, called WU-Net, to break the bottleneck. Beyond the autoencoder-like architecture, WU-Net learns an additional network from the pure or nearly-pure endmembers to correct the weights of another unmixing network towards a more accurate and interpretable unmixing solution, thus yielding a two-stream deep network. Experimental results conducted on two different datasets, one fully artificial simulation dataset and one simulated EnMap dataset generated from a real HyMap dataset, demonstrate the effectiveness and superiority of WU-Net over several state-of-the-art algorithms.