Dictionary Learning-Guided Deep Interpretable Network for Hyperspectral Change Detection

被引:8
作者
Zhao, Jingyu [1 ]
Xiao, Song [2 ,3 ]
Dong, Wenqian [1 ]
Qu, Jiahui [1 ]
Li, Yunsong [1 ]
机构
[1] Xidian Univ, State Key Lab Integrated Serv Network, Xian 710071, Peoples R China
[2] Beijing Elect Sci & Technol Inst, Dept Elect & Commun Engn, Beijing 100070, Peoples R China
[3] Xidian Univ, Sch Telecommun Engn, Xian 710071, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Dictionaries; Iterative methods; Feature extraction; Hyperspectral imaging; Noise reduction; Learning systems; Decoding; Change detection; deep neural network; dictionary learning; hyperspectral image (HIS); interpretable;
D O I
10.1109/LGRS.2023.3309138
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
Hyperspectral image (HSI) change detection is a technique to observe the change information between the multitemporal HSIs, which is currently considered a major focus of research in the field of remote-sensing intelligent interpretation. Most existing deep-learning-based methods have created satisfactory performance, but these methods lack transparency and have poor generalization. To tackle the problems outlined above, we propose a dictionary learning-guided deep interpretable network (DLDINet) for hyperspectral change detection, which unfolds a dictionary learning-based change detection model into an interpretable deep neural network. Specifically, we first design a dictionary learning-based change detection model, whose solution process can be decomposed into two iterative subproblems. Then, the mathematical model can be unfolded into a dual-branch deep neural network with two modules iterating with each other. Finally, the difference map of the coefficient's output from the ultimate stage is classified to obtain the change detection result. Experimental results prove that the proposed method has comparable or even better performance than state-of-the-art methods.
引用
收藏
页数:5
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