Hyper-spectral Images Classification Based on 3D Convolution Neural Networks for Remote Sensing

  • Zhiming MeiEmail author
  • Long Wang
  • Cen Guo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 972)


With the rapid development of hyper-spectral imaging techniques, hyper-spectral image classification has been applied to many tasks such as monitoring, astronomy and substance exploration. Hyper-spectral Images with rich spatial and spectral content is more difficult to be classified than common images with RGB channels. Many deep learning methods have ignored the context between spectral features when extracting spectral-spatial features of hyper-spectral images. So we implemented a 3D Convolution Neural Network model to extract correlated and effective features and improve the performance for Hyper-spectral Images classification. The hyper-spectral data set we use is the University of Pavia which has less training samples. So we exploited dropout and cross validation methods in the training process to avoid over fitting and we have extended the training samples by some transformation. The results of our experiments have shown that our model can generally get better results than some of the state-of-the-art methods.


Hyper-spectral image classification Convolution Neural Network Remote sensing Deep learning 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.ShanghaiTech UniversityShanghaiChina

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