Global random graph convolution network for hyperspectral image classification

doi:10.3390/rs13122285

CIOMP OpenIR

	Global random graph convolution network for hyperspectral image classification
	C. Zhang; J. Wang and K. Yao
	2021
发表期刊	Remote Sensing
ISSN	20724292
卷号	13 期号:12
摘要	Machine learning and deep learning methods have been employed in the hyperspectral image (HSI) classification field. Of deep learning methods, convolution neural network (CNN) has been widely used and achieved promising results. However, CNN has its limitations in modeling sample relations. Graph convolution network (GCN) has been introduced to HSI classification due to its demonstrated ability in processing sample relations. Introducing GCN into HSI classification, the key issue is how to transform HSI, a typical euclidean data, into non-euclidean data. To address this problem, we propose a supervised framework called the Global Random Graph Convolution Network (GR-GCN). A novel method of constructing the graph is adopted for the network, where the graph is built by randomly sampling from the labeled data of each class. Using this technique, the size of the constructed graph is small, which can save computing resources, and we can obtain an enormous quantity of graphs, which also solves the problem of insufficient samples. Besides, the random combination of samples can make the generated graph more diverse and make the network more robust. We also use a neural network with trainable parameters, instead of artificial rules, to determine the adjacency matrix. An adjacency matrix obtained by a neural network is more flexible and stable, and it can better represent the relationship between nodes in a graph. We perform experiments on three benchmark datasets, and the results demonstrate that the GR-GCN performance is competitive with that of current state-of-the-art methods. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
DOI	10.3390/rs13122285
URL	查看原文
收录类别	SCI ; EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65274
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	C. Zhang,J. Wang and K. Yao. Global random graph convolution network for hyperspectral image classification[J]. Remote Sensing,2021,13(12).
APA	C. Zhang,&J. Wang and K. Yao.(2021).Global random graph convolution network for hyperspectral image classification.Remote Sensing,13(12).
MLA	C. Zhang,et al."Global random graph convolution network for hyperspectral image classification".Remote Sensing 13.12(2021).

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Global random graph （1592KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览请求全文