A DUAL-STREAM CONVOLUTIONAL FEATURE FUSION NETWORK FOR HYPERSPECTRAL UNMIXING

被引:0
|
作者
Hua, Haoyue [1 ]
Li, Jie [1 ]
Wang, Ying [1 ]
Gao, Xinbo [1 ]
机构
[1] Xidian Univ, Sch Elect Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
来源
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年
关键词
Hyperspectral imagery; spectral unmixing; convolutional neural network(CNN);
D O I
10.1109/IGARSS52108.2023.10283276
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
As an important research element in the field of hyperspectral remote sensing, the purpose of hyperspectral unmixing is to decompose the mixed pixels in a hyperspectral image into endmembers and abundance. With the development of deep learning, it also has good prospects for application in the field of hyperspectral unmixing. Existing hyperspectral unmixing networks tend to focus only on the spectral information in the image which neglecting the spatial information. Therefore, a network based on dual-stream convolutional feature fusion for hyperspectral unmixing is proposed to make full use of the correlation between neighboring pixels in the paper, which includes three parts: dual-stream feature extraction, feature fusion and abundance estimation. The unmixing performance of the network is verified on two real datasets and exhibits better performance compared with other state-of-the-art methods.
引用
收藏
页码:7531 / 7534
页数:4
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