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Classification of Hyperspectral Data Using a Multi-Channel Convolutional Neural Network

  • Chen Chen
  • Jing-Jing Zhang
  • Chun-Hou Zheng
  • Qing Yan
  • Li-Na Xun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

In recent years, deep learning is widely used for hyperspectral image (HSI) classification, among them, convolutional neural network (CNN) is most popular. In this paper, we propose a method for hyperspectral data classification by multi-channel convolutional neural network (MC-CNN). In this framework, one dimensional CNN (1D-CNN) is mainly used to extract the spectral feature of hyperspectral images, two dimension CNN (2D-CNN) is mainly used to extract the spatial feature of hyperspectral images, three-dimensional CNN (3D-CNN) is mainly used to extract part of the spatial and spectral information. And then these features are merged and pull into the full connection layer. At last, using neural network classifiers like logistic regression, we can eventually get class labels for each pixel. For comparison and validation, we compare the proposed MC-CNN algorithm with the other three deep learning algorithms. Experimental results show that our MC-CNN-based algorithm outperforms these state-of-the-art algorithms. Showcasing the MC-CNN framework has huge potential for accurate hyperspectral data classification.

Keywords

Deep learning Hyperspectral image classification Convolutional neural network Full connection layer Logistic regression 

Notes

Acknowledgments

This work is supported by Anhui Provincial Natural Science Foundation (grant number 1608085MF 136), the National Science Foundation for China (Nos. 61602002 & 61572372).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Chen Chen
    • 1
  • Jing-Jing Zhang
    • 1
  • Chun-Hou Zheng
    • 2
  • Qing Yan
    • 1
  • Li-Na Xun
    • 1
  1. 1.College of Electrical Engineering and AutomationAnhui UniversityHefeiChina
  2. 2.College of Computer Science and TechnologyAnhui UniversityHefeiChina

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