Abstract
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.
This work was supported in part by the national key research and development program of china under grant 2017YFB0503001.
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References
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)
Goldstein, G.B.: False-alarm regulation in log-normal and Weibull clutter. IEEE Trans. Aerosp. Electron. Syst. AES-9(1), 84–92 (1973)
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)
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)
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)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Huang, J., Rathod, V., Sun, C., et al.: Speed/accuracy trade-offs for modern convolutional object detectors, pp. 3296–3297 (2016)
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)
Girshick, R.: Fast R-CNN. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448. IEEE, Santiago (2015)
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)
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)
Xu, F., Wang, H., Jin, Y.: Deep learning as applied in SAR target recognition and terrain classification. J. Radars 6(2), 136–148 (2017). https://doi.org/10.12000/jr16130
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
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)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018)
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Yue, B., Zhao, W., Han, S. (2020). SAR Ship Detection Method Based on Convolutional Neural Network and Multi-layer Feature Fusion. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_5
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