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Link to original content: https://doi.org/10.1007/s11432-019-2772-5
FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition | Science China Information Sciences Skip to main content
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FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition

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Abstract

Gaofen-3 (GF-3) is China’s first civil C-band fully Polarimetric spaceborne synthetic aperture radar (SAR) primarily missioned for ocean remote sensing and marine monitoring. This paper proposes an automatic sea segmentation, ship detection, and SAR-AIS matchup procedure and an extensible marine target taxonomy of 15 primary ship categories, 98 sub-categories, and many non-ship targets. The FUSAR-Ship high-resolution GF-3 SAR dataset is constructed by running the procedure on a total of 126 GF-3 scenes covering a large variety of sea, land, coast, river and island scenarios. It includes more than 5000 ship chips with AIS messages as well as samples of strong scatterer, bridge, coastal land, islands, sea and land clutter. FUSAR-Ship is intended as an open benchmark dataset for ship and marine target detection and recognition. A preliminary 8-type ship classification experiment based on convolutional neural networks demonstrated that an average of 79% test accuracy can be achieved.

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References

  1. Xu F, Jin Y Q, Moreira A. A preliminary study on SAR advanced information retrieval and scene reconstruction. IEEE Geosci Remote Sens Lett, 2016, 13: 1443–1447

    Article  Google Scholar 

  2. Paes R L, Lorenzzetti J A, Gherardi D F M. Ship detection using TerraSAR-X images in the campos basin (Brazil). IEEE Geosci Remote Sens Lett, 2010, 7: 545–548

    Article  Google Scholar 

  3. Liu Y, Yao L, Xiong W, et al. Fusion detection of ship targets in low resolution multi-spectral images. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016. 6545–6548

  4. Shuai T, Sun K, Wu X, et al. A ship target automatic detection method for high-resolution remote sensing. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016. 1258–1261

  5. Wang W, Fu Y, Dong F, et al. Semantic segmentation of remote sensing ship image via a convolutional neural networks model. IET Image Process, 2019, 55: 1016–1022

    Article  Google Scholar 

  6. Xu F, Wang H, Song Q, et al. Intelligent ship recongnition from synthetic aperture radar images. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018. 4387–4390

  7. An Q, Pan Z, You H. Ship detection in Gaofen-3 SAR images based on sea clutter distribution analysis and deep convolutional neural network. Sensors, 2018, 18: 334

    Article  Google Scholar 

  8. Ma M, Chen J, Liu W, et al. Ship classification and detection based on CNN using GF-3 SAR images. Remote Sens, 2018, 10: 2043

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Wang Y, Wang C, Zhang H, et al. Automatic ship detection based on retinanet using multi-resolution Gaofen-3 imagery. Remote Sens, 2019, 11: 531

    Article  Google Scholar 

  11. Guo Q, Wang H, Kang L, et al. Aircraft target detection from spaceborne SAR image. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2019. 1168–1171

  12. Fu S, Xu F, Jin Y-Q. Reciprocal translation between SAR and optical remote sensing images with cascaded-residual adversarial networks. 2019. ArXiv:1901.08236

  13. Shao Z Y, He J H, Feng S S. Extraction of a target in sea clutter via signal decomposition. Sci China Inf Sci, 2020, 63: 129301

    Article  Google Scholar 

  14. Long T, Liang Z N, Liu Q H. Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition. Sci China Inf Sci, 2019, 62: 040301

    Article  Google Scholar 

  15. Ao W, Xu F, Li Y, et al. Detection and discrimination of ship targets in complex background from spaceborne ALOS-2 SAR images. IEEE J Sel Top Appl Earth Observ Remote Sens, 2018, 11: 536–550

    Article  Google Scholar 

  16. Bandiera F, Orlando D, Ricci G. Advanced radar detection schemes under mismatched signal models. In: Synthesis Lectures on Signal Processing. Williston: Morgan & Claypool, 2009. 1–105

    Google Scholar 

  17. Liu G, Zhang Y S, Zheng X W, et al. A new method on inshore ship detection in high-resolution satellite images using shape and context information. IEEE Geosci Remote Sens Lett, 2014, 11: 617–621

    Article  Google Scholar 

  18. Chen H, Wang Q, Shen Y. Decision tree support vector machine based on genetic algorithm for multi-class classification. J Syst Eng Electron, 2011, 22: 322–326

    Article  Google Scholar 

  19. Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009. 248–255

  20. Everingham M, van Gool L, Williams C K I, et al. The pascal visual object classes (VOC) challenge. Int J Comput Vis, 2010, 88: 303–338

    Article  Google Scholar 

  21. Liu N, Cao Z, Cui Z, et al. Multi-scale proposal generation for ship detection in SAR images. Remote Sens, 2019, 11: 526

    Article  Google Scholar 

  22. Xia G, Bai X, Ding J, et al. DOTA: a large-scale dataset for object detection in aerial images. Computer science: computer vision and pattern recognition. 2017. ArXiv:1711.10398

  23. Zhu H, Chen X, Dai W, et al. Orientation robust object detection in aerial images using deep convolutional neural network. In: Proceedings of 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, 2015

  24. Cheng G, Han J, Zhou P, et al. Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS J Photogrammetry Remote Sens, 2014, 98: 119–132

    Article  Google Scholar 

  25. Cheng G, Han J. A survey on ob ject detection in optical remote sensing images. ISPRS J Photogrammetry Remote Sens, 2016, 117: 11–28

    Article  Google Scholar 

  26. Cheng G, Zhou P, Han J. Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images. IEEE Trans Geosci Remote Sens, 2016, 54: 7405–7415

    Article  Google Scholar 

  27. Chen S, Wang H, Xu F, et al. Target classification using the deep convolutional networks for SAR images. IEEE Trans Geosci Remote Sens, 2016, 54: 4806–4817

    Article  Google Scholar 

  28. Cho J H, Park C G. Multiple feature aggregation using convolutional neural networks for SAR image-based automatic target recognition. IEEE Geosci Remote Sens Lett, 2018, 15: 1882–1886

    Article  Google Scholar 

  29. Huang L, Liu B, Li B, et al. OpenSARShip: a dataset dedicated to sentinel-1 ship interpretation. IEEE J Sel Top Appl Earth Observ Remote Sens, 2018, 11: 195–208

    Article  Google Scholar 

  30. Li B, Liu B, Huang L, et al. OpenSARShip 2.0: a large-volume dataset for deeper interpretation of ship targets in Sentinel-1 imagery. In: Proceedings of 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSAR-DATA), Beijing, 2017. 1–5

  31. Bao M, Zhang J, Meng J M, et al. Construction and feature analysis of high resolution SAR ship sample set (in Chinese). Chin J Radio Sci, 2019, 34

  32. Wang Y, Wang C, Zhang H, et al. A SAR dataset of ship detection for deep learning under complex backgrounds. Remote Sens, 2019, 11: 765

    Article  Google Scholar 

  33. Liu J, Zhang Q. Overview of Gaofen-3 satellite applications (in Chinese). Satell Appl, 2018, (06): 12–16

  34. Song J, Oh K, Kim I, et al. Application of maritime AIS (automatic identification system) to ADS-B (automatic dependent surveillance-broadcast) transceiver. In: Proceedings of International Conference on Control, Automation and Systems, Gyeonggi-do, 2010. 2233–2237

  35. Pan J, Zheng X, Sun L, et al. Image segmentation based on 2D OTSU and simplified swarm optimization. In: Proceedings of 2016 International Conference on Machine Learning and Cybernetics (ICMLC), 2016. 1026–1030

  36. Hou X, Ao W, Xu F. End-to-end automatic ship detection and recognition in high-resolution Gaofen-3 spaceborne SAR images. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2019. 9486–9489

  37. Miller M L, Stone H S, Cox I J. Optimizing Murty’s ranked assignment method. IEEE Trans Aerosp Electron Syst, 1997, 33: 851–862

    Article  Google Scholar 

  38. Munkres J. Algorithms for the assignment and transportation problems. J Soc Industrial Appl Math, 1957, 5: 32–38

    Article  MathSciNet  MATH  Google Scholar 

  39. Yue J, Wang S. EM algorithm and its initialization in Gaussian mixture model based clustering. Microcomput Inf, 2006, 22: 244–246

    Google Scholar 

  40. He H, Lin Y, Chen F, et al. Inshore ship detection in remote sensing images via weighted pose voting. IEEE Trans Geosci Remote Sens, 2017, 55: 3091–3107

    Article  Google Scholar 

  41. Xu F, Wang H, Jin Y Q. Deep learning as applied in SAR target recognition and terrain classification (in Chinese). J Radars, 2017, 6: 136–148

    Google Scholar 

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Acknowledgements

This work was supported in part by National Key R&D Program of China (Grant No. 2017YFB0502703) and National Natural Science Foundation of China (Grant Nos. 61991422, 61822107).

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Authors and Affiliations

  1. Key Lab for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, 200433, China

    Xiyue Hou, Wei Ao, Qian Song, Haipeng Wang & Feng Xu

  2. Shanghai Center for Gaofen Data and Applications, Shanghai, 201109, China

    Jian Lai

Authors
  1. Xiyue Hou
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  2. Wei Ao
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  3. Qian Song
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  4. Jian Lai
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  5. Haipeng Wang
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  6. Feng Xu
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Corresponding author

Correspondence to Feng Xu.

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Hou, X., Ao, W., Song, Q. et al. FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition. Sci. China Inf. Sci. 63, 140303 (2020). https://doi.org/10.1007/s11432-019-2772-5

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