Fuzzy graph convolutional network for hyperspectral image classification

doi:10.1016/j.engappai.2023.107280

	Fuzzy graph convolutional network for hyperspectral image classification
	Xu, Jindong 1; Li, Kang 1,2; Li, Ziyi 1; Chong, Qianpeng 1; Xing, Haihua 3; Xing, Qianguo4 ; Ni, Mengying 1
发表期刊	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
ISSN	0952-1976
	2024
卷号	127 页码:13
关键词	Graph convolutional network Hyperspectral image Image classification Fuzzy logic Graph construction method
DOI	10.1016/j.engappai.2023.107280
通讯作者	Ni, Mengying([email protected])
英文摘要	-Graph convolutional network (GCN) has attracted much attention in the field of hyperspectral image classification for its excellent feature representation and convolution on arbitrarily structured non-Euclidean data. However, most state-of-the-art methods build a graph utilize the distance measure, which makes it challenging to fully characterize the complex relationship of hyperspectral remote sensing data. Moreover, the hyperspectral image usually has uncertainty introduced by the problems of the spectral variability and noise interference. This article uses fuzzy theory to optimize the GCN and thus solve the uncertainty problem in hyperspectral images, and presents a novel fuzzy graph convolutional network (F-GCN) for hyperspectral image classification. By calculating the fuzzy similarity of samples, a robust graph is first built rather than using the traditional Euclidean distance method, which allows a better representation of the complex relationship between hyperspectral remote sensing data. Furthermore, the proposed network introduces fuzzy layers into the model to cope with the ambiguity of the hyperspectral image. Finally, the classification results for three real-world hyperspectral data sets to show its feasibility and effectiveness in hyperspectral image classification.
资助机构	National Natural Science Foundation of China ; Shandong Provincial Natural Science Foundation of China
收录类别	SCI
语种	英语
研究领域[WOS]	Automation & Control Systems ; Computer Science ; Engineering
WOS记录号	WOS:001243181100001
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/35969
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
通讯作者	Ni, Mengying
作者单位	1.Yantai Univ, Sch Comp & Control Engn, Yantai 264005, Peoples R China 2.Quan Cheng Lab, Jinan 250100, Peoples R China 3.Hainan Normal Univ, Coll Informat Sci & Technol, Haikou 571158, Peoples R China 4.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
推荐引用方式 GB/T 7714	Xu, Jindong,Li, Kang,Li, Ziyi,et al. Fuzzy graph convolutional network for hyperspectral image classification[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2024,127:13.
APA	Xu, Jindong.,Li, Kang.,Li, Ziyi.,Chong, Qianpeng.,Xing, Haihua.,...&Ni, Mengying.(2024).Fuzzy graph convolutional network for hyperspectral image classification.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,127,13.
MLA	Xu, Jindong,et al."Fuzzy graph convolutional network for hyperspectral image classification".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 127(2024):13.