Blind Hyperspectral Unmixing using Dual Branch Deep Autoencoder with Orthogonal Sparse Prior

Blind hyperspectral unmixing has become an important task for hyperspectral applications. In this paper, we propose a dual branch autoencoder with a novel sparse prior to simultaneously extract endmembers and abundances from the raw HSI. The dual branch structure extends the linear mixing model by only modeling linear mixtures of the endmembers and treating the bilinear interactions as error. In this way, the proposed model doesn't require the assumptions of explicit forms of bilinear interactions. The proposed sparse prior, named as orthogonal sparse prior, is based on the key observation that the abundance vector of one pixel is very sparse, there are often no more than two non-zero elements. Different from the conventional norm-based sparse prior which assumes the abundance maps are independent, the orthogonal sparse prior explores the orthogonality between the abundance maps. Extensive experiments on two real datasets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods, with up to 50% improvements.