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作者:

Yang, Pan (Yang, Pan.) | Tong, Lei (Tong, Lei.) | Qian, Bin (Qian, Bin.) | Gao, Zheng (Gao, Zheng.) | Yu, Jing (Yu, Jing.) | Xiao, Chuangbai (Xiao, Chuangbai.)

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EI SCIE

摘要:

Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information. However, CNN can only perform convolution calculations on Euclidean datasets. To solve this problem, recently, the graph convolutional neural network (GCN) is proposed, which can be applied to the semisupervised HSI classification problem. However, the GCN is a direct push learning method, which requires all nodes to participate in the training process to get the node embedding. This may bring great computational cost for the hyperspectral data with a large number of pixels. Therefore, in this article, we propose an inductive learning method to solve the problem. It constructs the graph by sampling and aggregating (GraphSAGE) feature from a node's local neighborhood. This could greatly reduce the space complexity. Moreover, to enhance the classification performance, we also construct the graph using spectral and spatial information (spectra-spatial GraphSAGE). Experiments on several hyperspectral image datasets show that the proposed method can achieve better classification performance compared with state-of-the-art HSI classification methods. © 2008-2012 IEEE.

关键词:

Classification (of information) Convolution Convolutional neural networks Image classification Learning systems Spectroscopy

作者机构:

  • [ 1 ] [Yang, Pan]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Tong, Lei]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Qian, Bin]Traffic Management Research Institute, Ministry of Public Security, Wuxi; 214151, China
  • [ 4 ] [Gao, Zheng]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Yu, Jing]Traffic Management Research Institute, Ministry of Public Security, Wuxi; 214151, China
  • [ 6 ] [Xiao, Chuangbai]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China

通讯作者信息:

  • [tong, lei]faculty of information technology, beijing university of technology, beijing; 100124, china

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来源 :

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

ISSN: 1939-1404

年份: 2021

卷: 14

页码: 791-800

5 . 5 0 0

JCR@2022

ESI学科: GEOSCIENCES;

ESI高被引阀值:6

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 25

ESI高被引论文在榜: 0 展开所有

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