Spatial-Spectral Decoupling Framework for Hyperspectral Image Classification

被引：3
作者：
Fang, Jie [1 ]
Zhu, Zhijie [1 ]
He, Guanghua [1 ]
Wang, Nan [2 ]
Cao, Xiaoqian [3 ]
机构：
[1] Univ Posts & Telecommun, Sch Telecommun & Informat Engn, Xian 710121, Shaanxi, Peoples R China
[2] Univ Chinese Acad Sci, Beijing, Peoples R China
[3] Shaanxi Univ Sci & Technol, Sch Elect & Control Engn, Xian, Peoples R China
来源：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2023年 / 20卷
基金：
中国国家自然科学基金;
关键词：
Hyperspectral imaging; Encoding; Feature extraction; Collaboration; Convolutional neural networks; Training; Data preprocessing; Band selection (BS); collaborative decision-making; hyperspectral image classification; spatial-spectral decoupling (SD); NETWORK; CNN;
D O I：
10.1109/LGRS.2023.3277347
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
We present a spatial-spectral decoupling framework (SDF) to improve the performance of hyperspectral image classification, it mainly contains three modules, including data preprocessing, feature representation, and collaborative decision-making. Specifically, the data preprocessing module based on band selection (BS) network can effectively emphasize useful spectral bands while suppressing redundant ones. Besides, the feature representation module is based on spatial-spectral decoupling (SD) network to avoid information confusion between the spatial and the spectral domains. In addition, the collaborative decision-making mechanism based on joint optimization can maintain the discriminative properties of different branches and enhance mutual facilitation among them. Finally, the experimental results validate the effectiveness and superiority of our SDF.
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页数：5
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