SAR Ship Detection Method Based on Convolutional Neural Network and Multi-layer Feature Fusion

  • Bangzheng Yue
  • Wenda ZhaoEmail author
  • Song Han
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)


SAR ship detection plays an important role in marine traffic monitoring. Traditional SAR target detection methods are mostly based on intensity differences between target and clutter, which is limited especially in complex scenes, for instance coastal areas. In order to improve the detection performance in complex scenes, a SAR ship detection method based on convolutional neural network named LCMF is proposed in this paper. Firstly, a base network with low complexity is employed to extract features. Secondly, the ‘top-down’ approach is adopted to gradually fuse the semantically strong features, which is helpful for reducing false alarms, with the low-level high-resolution features to improve the detection performance on small targets. Finally, small-scaled anchor is designed to obtain region proposals, and these proposals are further fed to classification and regression network, which outputs the final detection results. Experiments on the sentinel-1A dataset demonstrate that the proposed method can detect ship targets in SAR images of complex scenes with high speed and accuracy.


Synthetic Aperture Radar Ship detection Convolutional neural network 


  1. 1.
    Eldarymli, K., Mcguire, P., Power, D., et al.: Target detection in synthetic aperture radar imagery: a state-of-the-art survey. J. Appl. Remote Sens. 7(7), 071598 (2013)CrossRefGoogle Scholar
  2. 2.
    Goldstein, G.B.: False-alarm regulation in log-normal and Weibull clutter. IEEE Trans. Aerosp. Electron. Syst. AES-9(1), 84–92 (1973)Google Scholar
  3. 3.
    Gao, G., Liu, L., Zhao, L., et al.: An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images. IEEE Trans. Geosci. Remote Sens. 47(6), 1685–1697 (2009)CrossRefGoogle Scholar
  4. 4.
    Zhao, H., Wang, Q., Huang, J., et al.: Method for inshore ship detection based on feature recognition and adaptive background window. J. Appl. Remote Sens. 8(1), 083608 (2014)CrossRefGoogle Scholar
  5. 5.
    Liang, Z., Yu, L., Yi, S.: Inshore ship detection via saliency and context information in high-resolution sar images. IEEE Geosci. Remote Sens. Lett. 13(12), 1870–1874 (2016)CrossRefGoogle Scholar
  6. 6.
    Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  7. 7.
    Huang, J., Rathod, V., Sun, C., et al.: Speed/accuracy trade-offs for modern convolutional object detectors, pp. 3296–3297 (2016)Google Scholar
  8. 8.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587. IEEE, Columbus (2014)Google Scholar
  9. 9.
    Girshick, R.: Fast R-CNN. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448. IEEE, Santiago (2015)Google Scholar
  10. 10.
    Ren, S.Q., He, K.M., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)CrossRefGoogle Scholar
  11. 11.
    Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C.Y., Berg, A.C.: SSD: single shot multibox detector. In: Proceedings of the 14th European Conference on Computer Vision (ECCV) 21–37. Springer, Amsterdam (2016)Google Scholar
  12. 12.
    Xu, F., Wang, H., Jin, Y.: Deep learning as applied in SAR target recognition and terrain classification. J. Radars 6(2), 136–148 (2017). Scholar
  13. 13.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)Google Scholar
  14. 14.
    Huang, L., Liu, B., Li, B., Guo, W., Yu, W., Zhang, Z., Yu, W.: OpenSARShip, a dataset dedicated to Sentinel-1 ship interpretation. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 11(1), 195–208 (2018)CrossRefGoogle Scholar
  15. 15.
    Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.Institute of Electronics, Chinese Academy of SciencesBeijingChina

Personalised recommendations