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Deep Autoencoder for Hyperspectral Unmixing via Global-Local Smoothing

Hyperspectral unmixing is to decompose the mixed pixels into pure spectral signatures (endmembers) and their proportions (abundances). Recently, deep learning based methods have been applied to enhance the representation ability of unmixing models by extracting joint spatial-spectral characteristics of the hyperspectral data. However, most deep learning based unmixing methods usually conduct global smoothing by convolutions on the whole hyperspectral imagery, which may ignore the variations within the imagery and result in over-smoothing. In this paper, we propose a deep network for hyperspectral unmixing based on a new Global-Local smoothing Autoencoder (GLA). GLA is an unsupervised model which aims at exploring the local homogeneity and the global self-similarity of hyperspectral imagery. The proposed GLA network mainly include two modules: a Local Continuous conditional random field Smoothing (LCS) module and a Global Recurrent Smoothing (GRS) module. In LCS, we propose a conditional random field based smoothing strategy to describe the joint spatial-spectral information within a local homogeneity region, which also reduces the risk of abundance maps boundary blurry. In GRS, we follow the self-similarity assumption for hyperspectral imagery, and develop a recurrent neural network structure to exploit potential long-distance dependency relationships among pixels. The GLA is compared with several state-of-the-art unmixing metheds on both real and synthetic data, and the abundance estimation results indicate that our method is promising. We will publish the code of GLA if the paper has the honor to be accepted.