Dual-Stream Attention Network for Hyperspectral Image Unmixing

Yufang Wang; Wenmin Wu; Lin Qi; Feng Gao

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arXiv · 2406.01644

Dual-Stream Attention Network for Hyperspectral Image Unmixing

Yufang Wang·Wenmin Wu·Lin Qi·Feng Gao

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Abstract

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.

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Yufang Wang, Wenmin Wu, Lin Qi, Feng Gao. 2024-06-03. Dual-Stream Attention Network for Hyperspectral Image Unmixing. https://arxiv.org/abs/2406.01644

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