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Deep learning (DL) has gained popularity in hyperspectral unmixing (HU) applications recently due to its powerful learning and data-fitting capabilities. As an unmixing baseline network, the autoencoder (AE) framework performs well in HU by automatically learning low-dimensional embeddings and reconstructing data. Nevertheless, there are spectral variability and nonlinear mixing problems in the highly mixed region of hyperspectral images, which can cause interference to structures using only AE. Therefore, inspired by the effectiveness of mask modeling, we propose a multi-scale convolutional mask network (MsCM-Net) for hyperspectral unmixing with two new strategies. First, we propose a mixed region mask strategy suitable for the HU task, and a multi-scale convolutional autoencoder is adopted as the unmixing baseline network to apply the mask strategy, making the method more robust in solving ill-posed unmixing problems. In addition, a new initialization strategy is used in which vertex component analysis (VCA) is combined with density-based spatial clustering of applications with noise (DBSCAN) to mitigate the impact of outliers and noise on initialization. The proposed MsCM-Net performs more accurately than state-of-the-art methods by comparison experiments on one synthetic and three real hyperspectral data sets. The effectiveness of the mixed region mask strategy and DBSCAN-VCA initialization is also demonstrated by ablation experiments.

Multi-Scale Convolutional Mask Network for Hyperspectral Unmixing