Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network

Haokui Zhang, Ying Li, Yuzhu Zhang, Qiang Shen

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Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of
autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image. Further in the proposed framework, we combine PCA on spectral dimension and autoencoder on the other two spatial dimensions to extract spectral-spatial
information for classification. The experimental results show that this framework achieves the highest classification accuracy among all methods, and outperforms classical classifiers such as SVM and PCA-based SVM
Original languageEnglish
Pages (from-to)438-447
JournalRemote Sensing Letters
Issue number5
Early online date18 Jan 2017
Publication statusPublished - 04 May 2017


  • autoencoders
  • deep learning
  • hyperspectral
  • image classification
  • neural networks
  • stacked autoencoders


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