基于对数域加性信号分解的时序SAR图像相干斑抑制方法

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

随着合成孔径雷达(SAR)在测绘带宽度、空间以及时间分辨率上的大幅提升,由不同时间获取的SAR图像配准得到的时间序列能更加精确地提供观测区域的动态变化信息。然而,相干斑噪声以及沿时间维度突变信号为后续的解译工作带来了严重挑战。尽管现有的主流方法可以对时序SAR图像的相干斑进行有效抑制,但沿时间维度突变信号会对去噪结果产生干扰。为更好地解决此问题,该文提出了一种基于对数域加性信号分解的方法,能同时抑制相干斑噪声并且对时序图像中的稳定信号和沿时间维度突变信号进行分离,从而消除突变信号对于去噪结果的影响。在仿真数据受到突变信号干扰的情况下,所提方法与其他主流方法相比,其去噪结果在峰值信噪比(PSNR)指标上取得了大约3 dB的提升。在哨兵1号数据上,所提方法能鲁棒地对时序图像中的相干斑噪声进行抑制,并且得到的突变信号成分也为后续的解译工作提供了参考数据。

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.