Abstract
Synthetic aperture radar (SAR) is one of the most widely used remote sensing monitoring methods for large-scale marine activities. Due to the influence of speckle noise, sea clutter and complex environment, ship detection of SAR images is still a challenging task. Based on the multi-layer selective cognition characteristics of the human visual system, we propose a ship target detection algorithm based on region growing and multi-scale saliency. First, the layered rough-fine land-sea segmentation is used to remove the effect of land scattering. Second, the non-subsampled Laplacian pyramid (NSLP) filter is applied to decompose the image at different scales. Then, the saliency region of the transformed coefficients is extracted by spectral residual (SR). And the constant false alarm rate (CFAR) algorithm is used to further filter the false alarm and extract target more accurately. Finally, saliency sub-images of different scales are fused to get the final detection results. Experimental results show that the algorithm not only effectively suppresses the influence of land and sea clutter, but also can improve the detection rate.
The first author is a student.
This research was funded by the Fundamental Research Funds for the Central Universities No. 2020YJS033; Natural Science Foundation of China under grant 61401308 and 61572063; Natural Science Foundation of Hebei Province under grant F2016201142, F2020201025 and F2018210148; Science research project of Hebei Province under grant BJ2020004; Opening Foundation of Machine vision Engineering Research Center of Hebei Province under grant 2018HBMV02.
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References
Arii, M., Koiwa, M., Aoki, Y.: Applicability of SAR to marine debris surveillance after the great East Japan Earthquake. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(5), 1729–1744 (2014)
Cheng, Y., Liu, B., Li, X., Nunziata, F., Xu, Q., Ding, X.: Monitoring of oil spill trajectories with COSMO-SkyMed X-Band SAR images and model simulation. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(7), 2895–2901 (2014)
Liu, G., Zhang, Y., Zheng, X., Sun, X., Fu, K., Wang, H.: A new method on inshore ship detection in high-resolution satellite images using shape and context information. IEEE Geosci. Remote Sens. Lett. 11(3), 617–621 (2014)
Zhu, C., Zhou, H., Wang, R., Guo, J.: A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features. IEEE Trans. Geosci. Remote Sens. 48(9), 3446–3456 (2010)
Gao, G., Shi, G.: CFAR ship detection in nonhomogeneous sea clutter using polarimetric SAR data based on the notch filter. IEEE Trans. Geosci. Remote Sens. 55(8), 4811–4824 (2017)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: 18th International Conference on Neural Information Processing Systems, pp. 155–162. Springer-Verlag (2005)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, pp. 1–8. IEEE (2007)
Yu, Y., Wang, B., Zhang, L.: Pulse discrete cosine transform for saliency-based visual attention. In: 2009 IEEE 8th International Conference on Development and Learning, Shanghai, China, pp. 1–6. IEEE (2009)
Cheng, M., Mitra, N., Huang, X., Torr, P., Hu, S.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)
Lu, Y., Jiang, T., Zang, Y.: Region growing method for the analysis of functional MRI data. NeuroImage 20(1), 455–465 (2003)
Yu, Y., Ding, Z., Wang, B., Zhang, L.: Visual attention-based ship detection in SAR images. Adv. Neural Netw. Res. Appl. 67, 283–292 (2010)
Liu, S., Cao, Z., Li, J.: A SVD-based visual attention detection algorithm of SAR image. In: Zhang, B., Mu, J., Wang, W., Liang, Q., Pi, Y. (eds.) The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. LNEE, vol. 246, pp. 479–486. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-00536-2_55
Hou, B., Yang, W., Wang, S., Hou, X.: SAR image ship detection based on visual attention model. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 2003–2006 (2013)
Bi, F., Zhu, B., Gao, L., Bian, M.: A visual search inspired computational model for ship detection in optical satellite images. IEEE Geosci. Remote Sens. Lett. 9(4), 749–753 (2012)
Wang, H., Xu, F., Chen, S.: Saliency detector for SAR images based on pattern recurrence. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 9(7), 2891–2900 (2016)
Wu, H., Zhou, Yu., Zhou, Y., Chen, X., Xiang, L., Li, Z.: Image segmentation using region growing based on 2D OTSU to selected seed points. J. Atmos. Environ. Opt. 8(6), 448–453 (2013). (in Chinese)
Yan, C., Liu, C.: A ship target detection method of SAR image based on saliency detection. J. Univ. Chin. Acad. Sci. 36(03), 401–409 (2019). (in Chinese)
Wang, Z., Du, L., Zhang, P., Li, L., Wang, F., Xu, S.: Visual attention-based target detection and discrimination for high-resolution SAR images in complex scenes. IEEE Trans. Geosci. Remote Sens. 56, 1–18 (2017)
Xiong, W., Xu, Y., Cui, Y., Li, Y.: Geometric feature extraction of ship in high-resolution synthetic aperture radar images. Acta Photonica Sinica 47(01), 55–64 (2018). (in Chinese)
Itti, L.: Models of bottom-up and top-down visual attention. California Institute of Technology, Computer Science Department Pasadena, CA, United States (2000)
Novak, L., Owirka, G., Brower, W.: The automatic target-recognition system in SAIP. Lincoln Lab. J. 10(2), 187–202 (1997)
Sun, X., Wang, A., Zhi, R., Sun, Y.: AIR-SARShip-1.0: high-resolution SAR ship detection dataset. J. Radars 8(6), 852–862 (2019). (in Chinese)
Cui, Y., Yang, J., Yamaguchi, Y.: CFAR ship detection in SAR images based on lognormal mixture models. In: 3rd International Asia-Pacific Conference on Synthetic Aperture Radar, Seoul, South Korea, pp. 1–3. IEEE (2011)
Cui, Y., Yang, J., Yamaguchi, Y., Singh, G., Park, S., Kobayashi, H.: On semiparametric clutter estimation for ship detection in synthetic aperture radar images. IEEE Trans. Geosci. Remote Sens. 51(5), 3170–3180 (2013)
Wang, Z., Du, L., Zhang, P., Li, L., Wang, F., Xu, S.: Visual attention-based target detection and discrimination for high-resolution SAR images in complex scenes. IEEE Trans. Geosci. Remote Sens. 56(4), 1855–1872 (2017)
Yan, C., Liu, C.: A ship target detection method of SAR image based on saliency detection. J. Univ. Chin. Acad. Sci. 36(3), 401–409 (2019). (in Chinese)
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Hu, Q., Hu, S., Liu, S. (2020). Ship Detection in SAR Images Based on Region Growing and Multi-scale Saliency. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_10
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