Volume 12 Issue 5
Oct.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(5): 1031-1043
KANG Jian, TONG Fengyu, BAI Yusong, et al. SAR time series despeckling based on additive signal component decomposition in logarithm domain[J]. Journal of Radars, 2023, 12(5): 1031–1043. doi: 10.12000/JR22242
Citation: KANG Jian, TONG Fengyu, BAI Yusong, et al. SAR time series despeckling based on additive signal component decomposition in logarithm domain[J]. Journal of Radars, 2023, 12(5): 1031–1043. doi: 10.12000/JR22242
shu

SAR Time Series Despeckling Based on Additive Signal Component Decomposition in Logarithm Domain

DOI: 10.12000/JR22242 CSTR: 32380.14.JR22242
  • 1.

    School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China

  • 2.

    School of Mathematical and Statistics, Northwestern Polytechnical University, Xi’an 710072, China

  • 3.

    Suzhou Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215123, China

  • 4.

    Suzhou Aerospace Information Research Institute, Suzhou 215123, China

  • 5.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

Funds:  The National Natural Science Foundation of China (62101371), Jiangsu Province Science Foundation for Youths (BK20210707)
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    Abstract
  • With the substantial improvement of Synthetic Aperture Radar (SAR) regarding swath width and spatial and temporal resolutions, a time series obtained by registering SAR images acquired at different times can provide more accurate information on the dynamic changes in the observed areas. However, inherent speckle noise and outliers along the temporal dimension in the time series pose serious challenges for subsequent interpretation tasks. Although existing state-of-the-art methods can effectively suppress the speckle noise in a SAR time series, outliers along the temporal dimension will interfere with the denoising results. To better solve this problem, this paper proposes an additive signal decomposition method in the logarithm domain that can suppress the speckle noise and separate stable data and outliers along the temporal dimension in a time series, thus eliminating the impact of outliers on the denoising results. When the simulated data are disturbed by outliers, the proposed method can achieve an approximately 3 dB improvement in the Peak Signal-to-Noise Ratio (PSNR) compared to the other state-of-the-art methods. On Sentinel-1 data, the proposed method robustly suppresses the speckle noise in a time series, and the obtained outliers along the temporal dimension provide reference data for subsequent interpretation tasks.

     

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