Volume 39 Issue 5
May  2020
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Haitao Lang, Yunhong Tao, Lihui Niu, Hongji Shi. A new scattering similarity based metric for ship detection in polarimetric synthetic aperture radar image[J]. Acta Oceanologica Sinica, 2020, 39(5): 145-150. doi: 10.1007/s13131-020-1563-7
Citation: Haitao Lang, Yunhong Tao, Lihui Niu, Hongji Shi. A new scattering similarity based metric for ship detection in polarimetric synthetic aperture radar image[J]. Acta Oceanologica Sinica, 2020, 39(5): 145-150. doi: 10.1007/s13131-020-1563-7

A new scattering similarity based metric for ship detection in polarimetric synthetic aperture radar image

doi: 10.1007/s13131-020-1563-7
Funds:  The National Natural Science Foundation of China under contract No. 61471024; the National Marine Technology Program for Public Welfare under contract No. 201505002.
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  • Corresponding author: E-mail: langht@mail.buct.edu.cn
  • Received Date: 2019-01-28
  • Accepted Date: 2019-05-13
  • Available Online: 2020-12-28
  • Publish Date: 2020-05-25
  • A new paradigm for ship detection in polarimetric synthetic aperture radar (Pol-SAR) image is presented. We firstly utilize the scattering similarity parameters to investigate the differences of scattering mechanism between ships and sea clutter. Based on these differences, we propose a novel ship detection metric, denoted as the scattering similarity based metric (SSM), to conduct ship detection task. The distribution model of SSM metric is investigated and modeled by kernel density estimation (KDE). Based on the statistical distribution, an adaptive constant false alarm rate (CFAR) detection scheme is implemented. We compare the proposed SSM with two classic polarimetric metrics, i.e., the polarimetric cross-entropy (PCE) and the reflection symmetry metric (RSM). The experimental results conducted on C-band RADARSAT-2 Pol-SAR data demonstrate the feasibility and advantage of the proposed SSM metric both in sea clutter modeling and in ship detection.
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  • [1]
    Bors A G, Nasios N. 2009. Kernel bandwidth estimation for nonparametric modeling. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(6): 1543–1555. doi: 10.1109/TSMCB.2009.2020688
    [2]
    Chen Jiong, Chen Yilun, Yang Jian. 2009. Ship detection using polarization cross-entropy. IEEE Geoscience and Remote Sensing Letters, 6(4): 723–727. doi: 10.1109/LGRS.2009.2024224
    [3]
    Jones M C, Marron J S, Sheather S J. 1996. A brief survey of bandwidth selection for density estimation. Journal of the American Statistical Association, 91(433): 401–407. doi: 10.1080/01621459.1996.10476701
    [4]
    Kullback S, Leibler R A. 1951. On information and sufficiency. The Annals of Mathematical Statistics, 22(1): 79–86. doi: 10.1214/aoms/1177729694
    [5]
    Lang Haitao, Zhang Jie, Zhang Ting, et al. 2014. Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery. Journal of Applied Remote Sensing, 8(1): 083623. doi: 10.1117/1.JRS.8.083623
    [6]
    Li Dong, Zhang Yunhua. 2015. Random similarity between two mixed scatterers. IEEE Geoscience and Remote Sensing Letters, 12(12): 2468–2472. doi: 10.1109/LGRS.2015.2484383
    [7]
    Liu C, Vachon P W, Geling G W. 2005. Improved ship detection with airborne polarimetric SAR data. Canadian Journal of Remote Sensing, 31(1): 122–131. doi: 10.5589/m04-056
    [8]
    Nunziata F, Migliaccio M, Brown C E. 2012. Reflection symmetry for polarimetric observation of man-made metallic targets at sea. IEEE Journal of Oceanic Engineering, 37(3): 384–394. doi: 10.1109/JOE.2012.2198931
    [9]
    Ouchi K. 2016. Current status on vessel detection and classification by synthetic aperture radar for maritime security and safety. In: Proceedings of the 38th Symposium on Remote Sensing for Environmental Sciences. Japan: Aichi.
    [10]
    Sheather S J. 2004. Density estimation. Statistical Science, 19(4): 588–597. doi: 10.1214/088342304000000297
    [11]
    Sun Yuan, Zhang Bo, Wang Chao, et al. 2012. Ship detection based on eigenvalue-eigenvector decomposition and OS-CFAR detector. In: Proceedings of 2012 International Conference on Computer Vision in Remote Sensing. Xiamen, China: IEEE, 350–355
    [12]
    Wang Wenguang, Ji Yu, Lin Xiaoxia. 2015. A novel fusion-based ship detection method from pol-SAR images. Sensors, 15(10): 25072–25089. doi: 10.3390/s151025072
    [13]
    Xi Yuyang, Lang Haitao, Tao Yunhong, et al. 2017. Four-component model-based decomposition for ship targets using polSAR data. Remote Sensing, 9(6): 621. doi: 10.3390/rs9060621
    [14]
    Yang Jian, Peng Yingning, Lin Shiming. 2001. Similarity between two scattering matrices. Electronics Letters, 37(3): 193–194. doi: 10.1049/el:20010104
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