Convolutional Autoencoder for Spatial-Spectral Hyperspectral Unmixing

In this paper, we present a deep learning based method for blind hyperspectral unmixing in the form of a fully convolutional autoencoder. The technique is the first to fully utilize the spatial structure of hyperspectral images (HSIs) for both endmember and abundance map estimation. The framework has many advantages over older methods as it works directly with patches of HSIs' and thus preserves the spatial structure while abundance maps arise naturally as feature maps of a hidden convolutional layer. We evaluate the proposed method using a real HSI and compare it to three state-of-the-art methods. The proposed method outperforms all the comparison methods in the experiments.