Lossless compression for hyperspectral image using deep recurrent neural networks

  • Jiqiang Luo
  • Jiaji WuEmail author
  • Shihui Zhao
  • Lei Wang
  • Tingfa Xu
Original Article


With the rapid development of hyperspectral remote sensing technology, the spatial resolution and spectral resolution of hyperspectral images are continually increasing, resulting in a continual increase in the scale of hyperspectral data. At present, hyperspectral lossless compression technology has reached a bottleneck. Simultaneously, the rise of deep learning has provided us with new ideas. Therefore, this paper examines the use of deep learning for the lossless compression of hyperspectral images. In view of the differential pulse code modulation (DPCM) method being insufficient for predicting spectral band information, the proposed method, called C-DPCM-RNN, uses a deep recurrent neural network (RNN) to improve the traditional DPCM method and improve the generalization ability and prediction accuracy of the model. The final experimental result shows that C-DPCM-RNN achieves better compression on a set of calibrated AVIRIS test images provided by the Multispectral and Hyperspectral Data Compression Working Group of the Consultative Committee for Space Data Systems in 2006. C-DPCM-RNN overcomes the limits of traditional methods in its performance on uncalibrated AVIRIS test images.


Hyperspectral images Lossless compression Deep learning 



This work is supported by National Natural Science Foundation of China nos. 61775175 and 61771378, Shenzhen Technology Project (JCYJ20170413152535587, JCYJ 20170307164023599, JSGG20170823091924128).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jiqiang Luo
    • 1
    • 2
  • Jiaji Wu
    • 3
    Email author
  • Shihui Zhao
    • 3
  • Lei Wang
    • 4
    • 5
  • Tingfa Xu
    • 1
  1. 1.School of OptoelectronicsBeijing Institute of TechnologyBeijingChina
  2. 2.Beijing Institute of Spacecraft System EngineeringBeijingChina
  3. 3.School of Electronic EngineeringXidian UniversityXi’anChina
  4. 4.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  5. 5.The Chinese University of Hong KongHong KongChina

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