Volume 13 Issue 2
Apr.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(2): 374-395
Wang Xuesong. Status and Prospects of Radar Polarimetry Techniques[J]. Journal of Radars, 2016, 5(2): 119-131. doi: 10.12000/JR16039
Citation: DENG Shasa, ZHANG Fan, YIN Qiang, et al. Refined ship feature characterization method of full-polarimetric synthetic aperture radar for visual interpretation[J]. Journal of Radars, 2024, 13(2): 374–395. doi: 10.12000/JR23078
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Refined Ship Feature Characterization Method of Full-polarimetric Synthetic Aperture Radar for Visual Interpretation

DOI: 10.12000/JR23078
  • 1.

    College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

  • 2.

    National Satellite Ocean Application Service, Beijing 100081, China

Funds:  The National Natural Science Foundation of China (62201027, 62271034)
More Information
  • Corresponding author: YIN Qiang, yinq@buct.edu.cn
  • Received Date: 2023-05-09
  • Rev Recd Date: 2023-06-11
    • Available Online: 2023-06-16
  • Publish Date: 2023-07-10
    Abstract
  • With advances in satellite technology, Polarimetric Synthetic Aperture Radar (PolSAR) now have higher resolution and better data quality, providing excellent data conditions for the refined visual interpretation of artificial targets. The primary method currently used is a multicomponent decomposition, but this method can result in pixel misdivision problems. Thus, we propose a non-fixed threshold division method for achieving advanced feature ship structure characterization in full-polarimetric SAR images. Yamaguchi decomposition can effectively identify the primary scattering mechanism and characterize artificial targets. Its modified volume scattering model is more consistent with actual data. The polarization entropy can serve as the target scattering mechanism at a specified equivalent point in the weakly depolarized state, which can effectively highlight the ship structure. This paper combines the three components of the Yamaguchi decomposition algorithm with the entropy, and divides it into a nine-classification plane with a non-fixed threshold. This method reduces category randomness generated by noise at the threshold boundary for complicated threshold treatments. Furthermore, the Mixed Scattering Mechanism (MSM) which is the region where both secondary scattering and single scattering are significant, was proposed to better match the scattering types of typical structures of vessels in the experiment. The Generalized Similarity Parameter (GSP) was used to further shorten the intra-class distance and perform iterative clustering using a modified GSP-Wishart classifier. This method improves the vessel distinguishability by enhancing the secondary and mixed scattering mechanisms. Finally, this paper uses full-polarimetric SAR data from a port in Shanghai, China, for the experiment. We collected and filtered ship information and optical data from this port through the Automatic Identification System (AIS) and matched them with the ships in full-polarimetric SAR images to verify the correct characterization of each vessel’s features. The experimental results show that the proposed method can effectively distinguish three types of vessels: bulk carriers, container ships and tankers.

     

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