Effective mitigation of atmospheric phase screen (APS) is crucial for synthetic aperture radar (SAR) interferometry in coastal regions. However, existing methods suffer from various limitations in estimating atmospheric delays, especially turbulent mixing components. As a solution, we propose a sparse convolution-based attention deep residual U-shaped network (sARU-Net), a submanifold sparse convolutional neural network (SSCNN), for adaptive estimation of APS from InSAR coherent pixels. Moreover, we propose a straightforward and effective iterative processing strategy for generating sample datasets for model training. The results obtained from the TerraSAR-X and Sentinel-1 datasets in two coastal regions of China demonstrate a reduction in the standard deviation of the interferograms by 77.7% and 68.3%, respectively, after applying our correction method. Additionally, the InSAR results align more closely with the leveling data. On the same dataset, our method shows superior performance compared to both the generic atmospheric correction online service for InSAR (GACOS) method and the linear model method. When compared to previous convolutional neural network (CNN, i.e., attention-based deep residual U-shaped network, ARU-Net) method, our approach exhibits better accuracy and more detail, and is 21% more computationally efficient for training, while requiring 24% less graphics processing unit (GPU) memory. The effectiveness of the proposed method shows that sparse convolution has great potential in mitigating the effects of APS in InSAR coherent pixels.

The noise in a tomographic synthetic aperture radar (Tomo-SAR) model is normally assumed to be independent and identically distributed (i.i.d.) Gaussian. In this paper, the correlated Tomo-SAR model is introduced by studying the effect of random residual phase and correlated additive Gaussian noise, and a realistic and general hybrid Cramér-Rao bound (HCRB) on elevation estimation is derived for such a model. Then, a simplified calculation of the HCRB is proposed when the bound of elevation is the main focus. Computer simulations are performed to analyze the proposed HCRB for elevation estimation. The results obtained from estimators based on compressive sensing and distributed compressive sensing show that the proposed HCRB can provide a more realistic bound than the CRB derived with the white additive noise and perfect phase compensation assumption. This is also validated through processing results on real data acquired by TerraSAR-X/Tandem-X sensors.

Persistent scatterers interferometric Synthetic Aperture Radar (PS-InSAR) is capable of precise topography measurement up to sub-meter scale and monitoring subtle deformation up to mm/year scale for all the radar image pixels with stable radiometric characteristics. As a representative PS-InSAR method, the Stanford Method for Persistent Scatterers (StaMPS) is widely used due to its high density of PS points for both rural and urban areas. However, when it comes to layover regions, which usually happen in urban areas, the StaMPS is limited locally. Moreover, the measurement points are greatly reduced due to the removal of adjacent PS pixels. In this paper, an improved StaMPS method, called IStaMPS, is proposed. The PS pixels are selected with high density by the improved PS selection strategy. Moreover, the topography information not provided in StaMPS can be accurately measured in IStaMPS. Based on the data acquired by TerraSAR-X/TanDEM-X over the Terminal 3 E (T3 E) site of Beijing Capital International Airport and the Chaobai River of Beijing Shunyi District, a comparison between StaMPS-retrieved results and IStaMPS-retrieved ones was performed, which demonstrated that the density of PS points detected by IStaMPS is increased by about 1.8 and 1.6 times for these two areas respectively. Through comparisons of local statistical results of topography estimation and mean deformation rate, the improvement granted by the proposed IStaMPS was demonstrated for both urban areas with complex buildings or man-made targets and non-urban areas with natural targets. In terms of the spatiotemporal deformation variation, the northwest region of T3 E experienced an exceptional uplift during the period from June 2012 to August 2015, and the maximum uplift rate is approximately 4.2 mm per year.

3-D phase unwrapping (PU) methods based on the 2-D linear temporal coherencemodel have been widely used in time-series interferometric synthetic aperture radar (TS-InSAR) for measuring topography and monitoring subtle deformation. However, the linear temporal coherencemodel can not characterize the coherence of highly coherent pixels accurately in seasonal deformation areas, where nonlinear deformation is deterministic and nonnegligible. Especially, for urban areas with groundwater or thermal dilation seasonal changes or permafrost regions, the nonlinear deformation is usually associated with periodic temperature changes. In this work, a general multi-component temporal coherence model, which considers multiple components including the seasonal deformation, is proposed for 3-D PU of seasonal deformation areas. Moreover, the uncertainty evaluation criterion, based on Cramér–Rao bound (CRB), is derived for TS-InSAR. The experimental results, obtained by applying the multi-component temporal coherence model to a data set acquired from January 2012 to February 2016 over the Beijing Capital International Airport area, confirm the effectiveness of the proposed method. High phase consistency, accurate corrected digital elevation model (DEM) and deformation information monitoring with high-density and high-coverage PS pixels are achieved. Under the same iterations and TS-InSAR procedure, the enhanced performance by the proposed model is illustrated by comparing with that of linear model in terms of phase consistency of 3-D phase unwrapping, PSCs selection at each step, and final results evaluation. In summary, the number of phase-consistency edges after 3-D PU is increased by about 15%, the number of final PS pixels selected with the same coherence threshold constraint is increased by about 10%, and more PS pixels provide a low uncertainty in residual topography, mean deformation velocity and seasonal amplitude estimation.

Tropospheric delays in spaceborne Interferometric Synthetic Aperture Radar (InSAR) can contaminate the measurement of small amplitude earth surface deformation. In this paper, a novel TXY-correlated method is proposed, where the main tropospheric delay components are jointly modeled in three dimensions, and then the long-scale and topography-correlated tropospheric delay components are corrected simultaneously. Moreover, the strategies of scale filtering and alternative iteration are employed to accurately retrieve all components of the joint model. Both the TXY-correlated method and the conventional phase-based methods are tested with a total of 25 TerraSAR-X/TanDEM-X images collected over the Chaobai River site and the Renhe Town of Beijing Shunyi District, where natural scenes and man-made targets are contained. A higher correction rate of tropospheric delays and a greater reduction in spatio-temporal standard deviations of time series displacement are observed after delay correction by the TXY-correlated method in both non-urban and urban areas, which demonstrate the superior performance of the proposed method.