Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (10): 2405-2414.DOI: 10.3778/j.issn.1673-9418.2102073

• Graphics and Image • Previous Articles    

Improved Two-Branch Capsule Network for Hyperspectral Image Classification

ZHANG Haitao, CHAI Simin+()   

  1. Software College, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Received:2021-03-01 Revised:2021-06-07 Online:2022-10-01 Published:2021-06-23
  • About author:ZHANG Haitao, born in 1974, Ph.D., profes-sor, M.S. supervisor, member of CCF. His resea-rch interests include machine learning, graphics and image processing, etc.
    CHAI Simin, born in 1996, M.S. candidate. His research interests include machine learning, graphics and image processing, etc.
  • Supported by:
    Pre-research Foundation of National Ministries and Commissions;Natural Science Foundation of Liaoning Province (General Program)(20170540426)


张海涛, 柴思敏+()   

  1. 辽宁工程技术大学 软件学院,辽宁 葫芦岛 125105
  • 通讯作者: + E-mail:
  • 作者简介:张海涛(1974—),男,博士,教授,硕士生导师,CCF会员,主要研究方向为机器学习、图形图像处理等。
  • 基金资助:


The method based on the dual-channel capsule network extracts spectral information and spatial informa-tion separately in two channels, which not only retains the feature extraction method of the dual-channel convolu-tional neural network, but also improves the classification accuracy. However, when researchers train the capsule network, the dynamic routing process generates a large number of training parameters because the hyperspectral image (HSI) usually consists of hundreds of channels. To address this limitation, 1D and 2D constraint windows are proposed to reduce the number of capsules from two extraction channels. It uses the capsule vector group as the calculation unit to perform convolution operations and reduce the amount of parameters and computational com-plexity of the capsule network. Based on this parameter reduction optimization method, a new dual-branch capsule neural network (DuB-ConvCapsNet-MRF) is proposed and applied to the task of hyperspectral image classifica-tion. In addition, in order to further improve the classification accuracy, Markov random field (MRF) is introduced to smooth the spatial region and the final output is got. The results of performing ablation experiments on two repre-sentative hyperspectral image datasets and comparing the proposed method with six existing classification methods show that DuB-ConvCapsNet-MRF is superior to other methods in classification performance, and effectively re-duces the cost of training capsule network.

Key words: remote sensing, hyperspectral image classification, capsule neural network, constraint window, Markov random field (MRF)



关键词: 遥感, 高光谱图像分类, 胶囊神经网络, 约束窗口, 马尔可夫随机场(MRF)

CLC Number: