Skip to main content
SpringerLink
Log in

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

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

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 via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

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

Authors and Affiliations

  1. Shanghai Key Laboratory of Intelligent Sensing and Recognition, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Juanping Zhao, Weiwei Guo, Zenghui Zhang & Wenxian Yu

Authors
  1. Juanping Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiwei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zenghui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenxian Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zenghui Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. 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

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-017-9405-6

Keywords

Search

Navigation