Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 381-385.DOI: 10.11772/j.issn.1001-9081.2019081385

• DPCS 2019 • Previous Articles     Next Articles

Hyperspectral band selection based on deep adversarial subspace clustering

Meng ZENG1,2, Bin NING1(), Zhihua CAI2, Qiong GU1   

  1. 1.Computer School,Hubei University of Arts and Science,Xiangyang Hubei 441053,China
    2.School of Computer Science,China University of Geosciences (Wuhan),Wuhan Hubei 430074,China
  • Received:2019-07-31 Revised:2019-09-21 Accepted:2019-09-23 Online:2019-09-29 Published:2020-02-10
  • Contact: Bin NING
  • About author:ZENG Meng,born in 1995, M. S. candidate. Her research interests include deep learning, data mining.
    CAI Zhihua, born in 1963, Ph. D., professor. His research interests include data mining, evolutionary calculation, parallel computing.
    GU Qiong, born in 1973, Ph. D., professor. Her research interests include intelligent computing, machine learning, network data mining.
  • Supported by:
    the Colleges and Universities’ Industry-University-Research Innovation Fund of Science and Technology Development Center of the Ministry of Education — New Generation Information Technology Innovation Project(2018A02028);the National Natural Science Foundation of China(61773355);the Fundamental Research Founds for National University,China University of Geosciences (Wuhan)(G1323541717);the Natural Science Foundation of Hubei Province(2018CFB528);the Open Research Project of Hubei Key Laboratory of Intelligent Geo-Information Processing(KLIGIP-2017B01)


曾梦1,2, 宁彬1(), 蔡之华2, 谷琼1   

  1. 1.湖北文理学院 计算机工程学院,湖北 襄阳 441053
    2.中国地质大学(武汉) 计算机学院,武汉 430074
  • 通讯作者: 宁彬
  • 作者简介:曾梦(1995—),女,湖北襄阳人,硕士研究生,主要研究方向:深度学习、数据挖掘
  • 基金资助:


HyperSpectral Image (HSI) consists of hundreds of bands with strong intra-band correlations between bands and high redundancy, resulting in dimensional disaster and increased classification complexity. Therefore, a Deep Adversarial Subspace Clustering (DASC) method was used for hyperspectral band selection, and Laplacian regularization was introduced to make the network performance more robust, which reduces the classification complexity under the premise of ensuring classification accuracy. A self-expressive layer was introduced between the encoder and the decoder to imitate the “self-expression” attribute of traditional subspace clustering, making full use of the spectral information and nonlinear feature transformation to obtain the relationships between the bands, and solving the problem that traditional band selection methods cannot consider spectral-spatial information simultaneously. At the same time, adversarial learning was introduced to supervise the sample representation of the auto-encoder and subspace clustering, so that the subspace clustering has better self-expression performance. In order to make the network performance more robust, Laplacian regularization was added to consider the manifold structure reflecting geometric information. Experimental results on two public hyperspectral datasets show that compared with several mainstream band selection methods, DASC method has higher accuracy, and the selected band subset of the method can satisfy application requirements.

Key words: HyperSpectral Image (HSI), band selection, Deep Adversarial Subspace Clustering (DASC), Laplacian regularization, deep learning



关键词: 高光谱图像, 波段选择, 深度对抗子空间聚类, 拉普拉斯正则化, 深度学习

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