Research on Target Recognition Technology of Satellite Remote Sensing Image Based on Neural Network

  • Qiang ZhangEmail author
  • Xiaonan Wang
  • Hexiang Tian
  • Yanan You
  • Peng Kong
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)


With the rapid development of satellite remote sensing technology, the resolution of satellite image is getting higher and higher, and more and more satellite data can be obtained on the ground. Traditional artificial image translation methods can not deal with massive data, and can not efficiently, quickly and accurately obtain the information of interested objects. In view of this problem, considering that the depth convolution neural network technology has achieved good results in the natural image target recognition, this paper uses the typical depth neural frame Faster R-CNN as the basic frame, and uses the image augmentation method to enhance the accuracy and generalization ability of the neural network model, and multi-resolution optical remote sensing image data to achieve automatic target recognition processing. The results show that the proposed method can translate images automatically and quickly, the recognition rate of ship and other targets is better than 75%.


Neural network Remote sensing Satellite Target recognition 


  1. 1.
    Yu, D., Deng, L.: Deep learning and its applications to signal and information processing. Signal Process. Mag. 28(1), 145–154 (2011)CrossRefGoogle Scholar
  2. 2.
    Lu, C., Tang, X.: Surpassing human-level face verification performance on LFW with GaussianFace. CoRR: abs/1404.3840 (2014)Google Scholar
  3. 3.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. Stateline: NIPS 2012, 1097–1105 (2012)Google Scholar
  4. 4.
    Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference oil Computer Vision and Pattern Recognition, pp. 580–587. IEEE Press, Piscataway (2014)Google Scholar
  5. 5.
    Chen, T.Y., Chen, J.Y., Zhao, H.P.: Ecognition-based ship detection on optical remote sensing images. Radio Eng. 43(11), 11–13 (2013). (in Chinese)Google Scholar
  6. 6.
    Wang, Y.Q., Ma, L., Tian, Y.: State-of-the–art of ship detection and recognition in optical remotely sensed imagery. Acta Automatica Sinica 37(9), 1029–1039 (2011). (in Chinese)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Qiang Zhang
    • 1
    Email author
  • Xiaonan Wang
    • 1
  • Hexiang Tian
    • 1
  • Yanan You
    • 2
  • Peng Kong
    • 1
  1. 1.Beijing Institute of Spacecraft System EngineeringBeijingChina
  2. 2.Beijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations