School of Electronic, Electrical and Communication Engineering, Chinese Academy of Sciences, Beijing 100190, China.
Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2020 Jan 19;20(2):551. doi: 10.3390/s20020551.
Interferometric phase filtering is a crucial step in multibaseline interferometric synthetic aperture radar (InSAR). Current multibaseline interferometric phase filtering methods mostly follow methods of single-baseline InSAR and do not bring its data superiority into full play. The joint filtering of multibaseline InSAR based on statistics is proposed in this paper. We study and analyze the fourth-order statistical quantity of interferometric phase: kurtosis. An empirical assumption that the kurtosis of interferograms with different baselines keeps constant is proposed and is named as the baseline-invariant property of kurtosis in this paper. Some numerical experiments and rational analyses confirm its validity and universality. The noise level estimation of nature images is extended to multibaseline InSAR by dint of the baseline-invariant property of kurtosis. A filtering method based on the non-subsampled shearlet transform (NSST) and Wiener filter with estimated noise variance is proposed then. Firstly, multi-scaled and multi-directional coefficients of interferograms are obtained by NSST. Secondly, the noise variance is represented as the solution of a constrained non-convex optimization problem. A pre-thresholded Wiener filtering with estimated noise variance is employed for shrinking or zeroing NSST coefficients. Finally, the inverse NSST is utilized to obtain the filtered interferograms. Experiments on simulated and real data show that the proposed method has excellent comprehensive performance and is superior to conventional single-baseline filtering methods.
干涉相位滤波是多基线干涉合成孔径雷达（InSAR）中的关键步骤。目前的多基线干涉相位滤波方法大多沿用单基线 InSAR 的方法，未能充分发挥其数据优势。本文提出了基于统计的多基线 InSAR 联合滤波。我们研究和分析了干涉相位的四阶统计量：峰度。提出了干涉图具有不同基线的峰度保持不变的经验假设，并在本文中命名为峰度的基线不变特性。一些数值实验和合理分析证实了其有效性和普遍性。通过峰度的基线不变特性，将自然图像的噪声水平估计扩展到多基线 InSAR。然后提出了一种基于非下采样剪切波变换（NSST）和估计噪声方差的维纳滤波器的滤波方法。首先，通过 NSST 获得干涉图的多尺度和多方向系数。其次，噪声方差表示为约束非凸优化问题的解。使用带估计噪声方差的预阈值维纳滤波对 NSST 系数进行收缩或置零。最后，利用逆 NSST 得到滤波后的干涉图。模拟和真实数据的实验表明，所提出的方法具有优异的综合性能，优于传统的单基线滤波方法。
