In Sentinel-1 terrain observation with progressive scan (TOPS) mode, this azimuth sweeping introduces an extra high-frequency Doppler term into the impulse response function, which requires the azimuth co-registration accuracy of 0.001 pixels to make the interferometric phase difference of adjacent bursts less than 3°. The enhanced spectral diversity (ESD) and developing versions are usually used to correct such residual azimuth misregistration. However, in fast decorrelation scenario, the distributed scatterers (DSs)-based ESDs need to perform lots of complex data processing to reduce the decorrelation impact on high-precision co-registration, including multilooking, spatial filtering, network construction, and least square adjustment. The DS-based co-registration methods are complicated and inefficient, which is not suitable for big SAR data processing. Therefore, this paper proposes a persistent scatterers-based ESD (PS-ESD): to estimate the amplitude dispersion index (ADI) with double samples over the burst overlap regions and select the satisfied PS pixels to compute the double differential phase under the one-master interferometric framework for correcting the time-series azimuth shifts. Based on the Sentinel-1 TOPS SAR data over the mountainous area in Three Gorges, China, experimental results have demonstrated that the proposed PS-ESD can achieve higher co-registration accuracy and faster convergence rate, especially in the case of dynamic co-registration of newly added TOPS images.

With the launch of various multipolarimetric satellites, many scholars have introduced the persistent scatterer (PS)-oriented polarimetric optimization methods and extended the persistent scatterer interferometry (PSI) method to multipolarimetric data configuration, called polarimetric PSI (PolPSI) technology. Most PolPSI methods mainly take the amplitude dispersion index (ADI) as the optimization criterion and evaluate the temporal amplitude stationarity of each polarimetric channel for finding an optimal one. However, due to the unstable statistical characteristics of the quality indicator, many non-PS pixels are easily mistaken for the PS candidates (PSCs), and the performance of interferometric phase optimization is also limited. To overcome these restrictions, in this article, a novel PolPSI method is proposed based on the following two improved innovations. First, in terms of PSC selection, the trace moment (TM)-based statistical properties of time-series polarimetric coherency matrices are utilized for selecting the scatterers with the temporal polarimetric stationarity. Second, in terms of interferometric phase optimization, all interferometric coherency matrices of multipolarization channels are added up together to construct the total power (TP) interferogram for suppressing the effect of speckle noise and decorrelation. In the experiment, 13 scenes of quad-polarization ALOS PALSAR-1 image are selected to verify the algorithm’s effectiveness. The experimental results demonstrate that the proposed PolPSI method can better improve the deformation monitoring performance in three aspects than both the single-polarimetric HH and traditional exhaustive search polarimetric optimization (ESPO) methods, including phase quality improvement, density of PSs, and computational efficiency.

The traditional nonlocal estimations have been demonstrated to be effective and widely used in polarimetric synthetic aperture radar interferometry (PolInSAR) data. However, there still exist some problems about two key steps: 1) in the homogeneous pixels selection step, the regular square (RS) patches matching strategy shows the limited performance in textured area and 2) in the central pixel value estimation from the selected pixels, the well-known Lee estimator, which only uses the intensity statistic, tends to be unstable. To overcome these restrictions, we put forward two robust strategies and then propose an improved PolInSAR complex coherence nonlocal estimation: 1) the shape-adaptive (SA) patch is utilized for flexibly matching the similar pixels in a large search window, which is constructed by combining the likelihood ratio test (LRT) and the region growing (RG) algorithm and 2) the trace-moment-based nonlocal reduced bias (TMB-NLRB) estimator is employed, which considers the interchannel correlations and evaluates more accurately the homogeneity level between the selected pixels. The denoising effect of both strategies is quantitatively analyzed on the simulated data set, and the proposed algorithm is compared with classical estimation algorithms on a TerraSAR-X/TanDEM-X PolInSAR data set. These experimental results show that the proposed method provides better performance in speckle reduction, detail preservation, and complex coherence estimation.