Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A coupled convolutional neural network for small and densely clustered ship detection in SAR images

  • 587 Accesses

  • 15 Citations

Abstract

Ship detection from synthetic aperture radar (SAR) imagery plays a significant role in global marine surveillance. However, a desirable performance is rarely achieved when detecting small and densely clustered ship targets, and this problem is difficult to solve. Recently, convolutional neural networks (CNNs) have shown strong detection power in computer vision and are flexible in complex background conditions, whereas traditional methods have limited ability. However, CNNs struggle to detect small targets and densely clustered ones that exist widely in many SAR images. To address this problem while preserving the good properties for complex background conditions, we develop a coupled CNN for small and densely clustered SAR ship detection. The proposed method mainly consists of two subnetworks: an exhaustive ship proposal network (ESPN) for ship-like region generation from multiple layers with multiple receptive fields, and an accurate ship discrimination network (ASDN) for false alarm elimination by referring to the context information of each proposal generated by ESPN. The motivation in ESPN is to generate as many ship proposals as possible, and in ASDN, the goal is to obtain the final results accurately. Experiments are evaluated on two data sets. One is collected from 60 wide-swath Sentinel-1 images and the other is from 20 GaoFen-3 (GF-3) images. Both data sets contain many ships that are small and densely clustered. The quantitative comparison results illustrate the clear improvements of the new method in terms of average precision (AP) and F1 score by 0.4028 and 0.3045 for the Sentinel-1 data set compared with the multi-step constant false alarm rate (CFAR-MS) method. The values are verified as 0.2033 and 0.1522 for the GF-3 data set. In addition, the new method is demonstrated to be more efficient than CFAR-MS.

This is a preview of subscription content, log in to check access.

References

  1. 1

    Wang S G, Wang M, Yang S Y, et al. New hierarchical saliency filtering for fast ship detection in high-resolution SAR images. IEEE Trans Geosci Remote Sens, 2017, 55: 351–362

  2. 2

    Gao G, Shi G T. CFAR ship detection in nonhomogeneous sea clutter using polarimetric SAR data based on the notch filter. IEEE Trans Geosci Remote Sens, 2017, 55: 4811–4824

  3. 3

    Zeng T, Zhang T, TianWM, et al. A novel subsidence monitoring technique based on space-surface bistatic differential interferometry using GNSS as transmitters. Sci China Inf Sci, 2015, 58: 062304

  4. 4

    Ma L, Chen L, Zhang X J, et al. A waterborne salient ship detection method on SAR imagery. Sci China Inf Sci, 2015, 58: 089301

  5. 5

    Crisp D. The state-of-the-art in ship detection in synthetic aperture radar imagery. Org Lett, 2004, 35: 2165–2168

  6. 6

    Wackerman C C, Friedman K S, Pichel W G, et al. Automatic detection of ships in RADARSAT-1 SAR imagery. Canadian J Remote Sens, 2001, 27: 568–577

  7. 7

    Ferrara M N, Torre A. Automatic moving targets detection using a rule-based system: comparison between different study cases. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium Proceedings, Seattle, 1998. 1593–1595

  8. 8

    Wang C L, Bi F K, Zhang W P, et al. An intensity-space domain CFAR method for ship detection in HR SAR images. IEEE Geosci Remote Sens Lett, 2017, 14: 529–533

  9. 9

    Bi H, Zhang B, Zhu X X, et al. L1-regularization-based SAR imaging and CFAR detection via complex approximated message passing. IEEE Trans Geosci Remote Sens, 2017, 55: 3426–3440

  10. 10

    Iervolino P, Guida R, Whittaker P. A novel ship-detection technique for Sentinel-1 SAR data. In: Proceedings of the 5th Asia-Pacific Conference on Synthetic Aperture Radar, Singapore, 2015. 797–801

  11. 11

    Feng J, Ma L, Bi F K, et al. A coarse-to-fine image registration method based on visual attention model. Sci China Inf Sci, 2014, 57: 122302

  12. 12

    Wu X M, Du M N, Chen W H, et al. Salient object detection via region contrast and graph regularization. Sci China Inf Sci, 2016, 59: 032104

  13. 13

    Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, 2012. 1097–1105

  14. 14

    Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intel, 2017, 39: 1137–1149

  15. 15

    Dai J F, Li Y, He K M, et al. R-FCN: object detection via region-based fully convolutional networks. In: Proceedings of the 30th Conference on Neural Information Processing Systems, Barcelona, 2016. 379–387

  16. 16

    Bell S, Zitnick C L, Bala K, et al. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016. 2874–2883

  17. 17

    Li X, Zhao L M, Wei L N, et al. DeepSaliency: multi-task deep neural network model for salient object detection. IEEE Trans Image Process, 2016, 25: 3919–3930

  18. 18

    Girshick R. Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, Santiago, 2015. 1440–1448

  19. 19

    Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector. In: Proceedings of European Conference on Computer Vision, Amsterdam, 2016. 21–37

  20. 20

    Cai Z W, Fan Q F, Feris R S, et al. A unified multi-scale deep convolutional neural network for fast object detection. In: Proceedings of European Conference on Computer Vision, Amsterdam, 2016. 354–370

  21. 21

    Lin T Y, Dollar P, Girshick R, et al. Feature pyramid networks for object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017. 936–944

  22. 22

    Xiang Y, Choi W, Lin Y Q, et al. Subcategory-aware convolutional neural networks for object proposals and detection. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision, Santa Rosa, 2017. 924–933

  23. 23

    Zhai L, Li Y, Su Y. Inshore ship detection via saliency and context information in high-resolution SAR images. IEEE Geosci Remote Sens Lett, 2016, 13: 1870–1874

  24. 24

    Zhu J W, Qiu X L, Pan Z X, et al. An improved shape contexts based ship classification in SAR images. Remote Sens, 2017, 9: 145

  25. 25

    Schmidhuber J. Deep learning in neural networks: an overview. Neur Netw, 2015, 61: 85–117

  26. 26

    Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. ArXiv:1409.1556

  27. 27

    Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 580–587

  28. 28

    Bottou L. Large-scale machine learning with stochastic gradient descent. In: Proceedings of the 19th International Conference on Computational Statistics, Paris, 2010. 177–186

  29. 29

    Neubeck A, van Gool L. Efficient non-maximum suppression. In: Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, 2006

  30. 30

    Jia Y Q, Shelhamer E, Donahue J, et al. Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, 2014. 675–678

  31. 31

    Zuhlke M, Fomferra N, Brockmann C, et al. SNAP (sentinel application platform) and the ESA Sentinel-3 Toolbox. In: Proceedings of Sentinel-3 for Science Workshop, Venice, 2015

  32. 32

    Pan Z X, Liu L, Qiu X L, et al. Fast vessel detection in Gaofen-3 SAR images with ultrafine strip-map mode. Sensors, 2017, 17: 1578

  33. 33

    Philbin J, Chum O, Isard M, et al. Object retrieval with large vocabularies and fast spatial matching. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, 2007

  34. 34

    Flach P, Kull M. Precision-recall-gain curves: PR analysis done right. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, 2015. 838–846

  35. 35

    Qin X X, Zhou S L, Zou H X, et al. A CFAR detection algorithm for generalized gamma distributed background in high-resolution SAR images. IEEE Geosci Remote Sens Lett, 2013, 10: 806–810

Download references

Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant No. 61331015) and China Postdoctoral Science Foundation (Grant No. 2015M581618). The authors are grateful to thank Prof. T. K. Truong for his helpful comments and suggestions that significantly improved this manuscript.

Author information

Correspondence to Zenghui Zhang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhao, J., Guo, W., Zhang, Z. et al. A coupled convolutional neural network for small and densely clustered ship detection in SAR images. Sci. China Inf. Sci. 62, 42301 (2019). https://doi.org/10.1007/s11432-017-9405-6

Download citation

Keywords

  • SAR image
  • ship detection
  • CNN
  • exhaustive ship proposal network (ESPN)
  • accurate ship discrimination network (ASDN)