DEEP BLIND UNMIXING USING MINIMUM SIMPLEX CONVOLUTIONAL NETWORK

Abstract

This paper proposes a deep blind hyperspectral unmixing network for datasets without pure pixels called minimum simplex convolutional network (MiSiCNet). MiSiCNet is the first deep learning-based blind unmixing method proposed in the literature which incorporates both spatial and geometrical information of the hyperspectral data, in addition to the spectral information. The proposed convolutional encoder-decoder architecture incorporates the spatial information using convolutional filters and implicitly applying a prior on the abundances. We added a minimum simplex volume penalty term to the loss function to exploit the geometrical information. We evaluate the performance of MiSiCNet on simulated and real datasets. The experimental results confirm the robustness of the proposed method to both noise and absence of pure pixels. Additionally, MiSiCNet considerably outperforms the state-of-the-art unmixing approaches. The results are given in terms of spectral angle distance in degree for the endmember estimation, and root mean square error in percentage for the abundance estimation. MiSiCNet was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/MiSiCNet.