Dual-Stream Attention Network for Hyperspectral Image Unmixing

Autor:	Wang, Yufang, Wu, Wenmin, Qi, Lin, Gao, Feng
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by IEEE IGARSS 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.01644 Zobrazit plný text záznamu View this record from Arxiv