Research on Spatial Target Classification and Recognition Technology Based on Deep Learning

  • Yujia PangEmail author
  • Zhi Li
  • Bo Meng
  • Zhimin Zhang
  • Longfei Huang
  • Jianbin Huang
  • Xu Han
  • Yin Wang
  • Xiaohui Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11743)


With the progress of human space technology, mankind stepped into more distant space. Due to the long distance from the earth in deep space exploration, higher requirements were put forward for intelligent cognition of targets and environment. In this paper, the space target classification and recognition technology based on deep learning was studied by taking the classification of three types of satellites as an example. A satellite simulation sample set for deep learning was established, and a ResNet multi-layer convolutional neural network model suitable for spatial target characteristics was constructed. The training and test of satellite intelligent classification were completed, and the feature extraction results of the neural network were visualized. The accuracy rate of satellite classification identification for the remote sensing satellites, communication satellites and navigation satellites reached 90%, which provided a reference for the development of intelligent classification and identification technology of space targets in the field of deep space exploration.


Deep space exploration Intelligent cognition Deep learning 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Qian Xuesen Laboratory of Space TechnologyBeijingPeople’s Republic of China
  2. 2.China Academy of Space TechnologyBeijingPeople’s Republic of China

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