  Hyperspectral image (HSI) contains abundant spatial and spectral information,
making it highly valuable for unmixing. In this paper, we propose a Dual-Stream
Attention Network (DSANet) for HSI unmixing. The endmembers and abundance of a
pixel in HSI have high correlations with its adjacent pixels. Therefore, we
adopt a "many to one" strategy to estimate the abundance of the central pixel.
In addition, we adopt multiview spectral method, dividing spectral bands into
multiple partitions with low correlations to estimate abundances. To aggregate
the estimated abundances for complementary from the two branches, we design a
cross-fusion attention network to enhance valuable information. Extensive
experiments have been conducted on two real datasets, which demonstrate the
effectiveness of our DSANet.
