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Ocean wave inversion based on the velocity bunching modulation of SAR image
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Qiaohui Xu1, Yanmin Zhang1, *, Yunhua Wang1, 2
Haiyang Xuebao | 2021, 43(12) : 111 - 121
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Haiyang Xuebao | 2021, 43(12): 111-121
• Article •
Ocean wave inversion based on the velocity bunching modulation of SAR image
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Qiaohui Xu1, Yanmin Zhang1, *, Yunhua Wang1, 2
Affiliations
  • 1College of Information Science & Engineering, Ocean University of China, Qingdao 266100, China
  • 2Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
Published: 2021-12-30 doi: 10.12284/hyxb2021103
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The effects of three kinds of modulation (the tilt modulation, the hydrodynamic modulation and the velocity bunching modulation) on sea wave SAR (synthetic aperture radar) image are compared and analyzed firstly. The results show that the velocity bunching modulation has the most significant effect on SAR images. In addition, due to the inherent speckle noise in the SAR image, it is difficult to filter or suppress the noise in the range of low wavenumber. And the inversion of wave spectrum by classical MPI (max-planck institute) method will result in the larger spectral value in the range of low wavenumber. Referring to the classical MPI inversion algorithm, an azimuth slope spectrum and SWH (significant wave height) inversion algorithm based on the velocity bunching modulation are proposed in this paper. Comparing the SWH obtained by the classical MPI method, the co-polarization modulation method and the algorithm proposed in this paper with the buoy data, one can find that the mean square error between the SWH retrieved by the algorithm proposed in this paper and the SWH obtained from buoy data is 0.79 m, which is the smallest among the three methods.

the velocity bunching modulation  /  ocean wave inversion  /  speckle noise  /  significant wave height
Qiaohui Xu, Yanmin Zhang, Yunhua Wang. Ocean wave inversion based on the velocity bunching modulation of SAR image[J]. Haiyang Xuebao, 2021 , 43 (12) : 111 -121 . DOI: 10.12284/hyxb2021103
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Year 2021 volume 43 Issue 12
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Article Info
doi: 10.12284/hyxb2021103
  • Receive Date：2020-10-29
  • Online Date：2026-02-26
  • Published：2021-12-30
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  • Received：2020-10-29
  • Revised：2021-05-24
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Affiliations
    1College of Information Science & Engineering, Ocean University of China, Qingdao 266100, China
    2Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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