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| 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
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SAR Time Series Despeckling Based on Additive Signal Component Decomposition in Logarithm Domain
doi: 10.12000/JR22242
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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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References
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- Figure 1. SAR amplitude time series with outliers along the temporal dimension
- Figure 2. Flow chart of the proposed method
- Figure 3. Simulation data used in this paper
- Figure 4. Speckle suppression results of the simulated data
- Figure 5. Parameter sensitivity analyses of the proposed method
- Figure 6. Sensitivity analysis of the proposed method with respect to the number of images
- Figure 7. Speckle suppression results for Sentinel-1 data in the Shanghai offshore region
- Figure 8. Speckle suppression results for Sentinel-1 data in Shanghai pudong airport region
- Figure 9. Comparison of image edge extraction results in offshore areas
- Figure 10. Comparison of image edge extraction results of ship region