COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health. To study the impact of socioeconomic factors on COVID transmission, we first propose a spatial-temporal model to examine the socioeconomic heterogeneity and spatial correlation of COVID-19 transmission at the community level. Second, to assess the individual risk of severe COVID-19 outcomes after a positive diagnosis, we propose a dynamic, varying-coefficient model that integrates individual-level risk factors from electronic health records (EHRs) with community-level risk factors. The underlying neighborhood prevalence of infections (both symptomatic and pre-symptomatic) predicted from the previous spatial-temporal model is included in the individual risk assessment so as to better capture the background risk of virus exposure for each individual. We design a weighting scheme to mitigate multiple selection biases inherited in EHRs of COVID patients. We analyze COVID transmission data in New York City (NYC, the epicenter of the first surge in the United States) and EHRs from NYC hospitals, where time-varying effects of community risk factors and significant interactions between individual- and community-level risk factors are detected. By examining the socioeconomic disparity of infection risks and interaction among the risk factors, our methods can assist public health decision-making and facilitate better clinical management of COVID patients.

The multitemporal interferometric synthetic aperture radar (InSAR) technique is a potential tool for measuring digital elevation models and surface deformation. It has the advantage of high precision, competitive spatial resolution, and wide coverage. To improve the accuracy of the final results, some adaptive multilook strategies have been proposed in which the identification of statistically homogeneous pixels (SHPs) is the key task. However, these methods are not always reliable in the case of small data sets. To improve this reliability, SHPs are identified based on the adaptive joint data vector comprising of temporal sample and spatial information in this letter. Additionally, the formulation of adaptive joint data vector is combined with local spatial features of SAR images. The presented adaptive multilook approach can be used in many interferometric applications, such as InSAR data filtering and coherence estimation. Experiments on six TerraSAR-X stripmap images of Tianjin in China validate the feasibility and effectiveness of the proposed approach.

It has been proven that SqueeSAR technique has validated the potential to increase the density of measure points (MP) involving persistent scatter (PS) and distributed scatter (DS) candidate in nonurban areas. In SqueeSAR, DS candidate exhibiting high extended temporal coherence can be processed jointly with PS for deformation estimation after phase estimation by the phase triangulation algorithm (PTA). The PTA extracts phase values of DS by using all possible interferograms under the Gaussian scattering assumption. However, the statistics hypothesis, Gaussian random variable, is no more applicable, with radar resolution increasing. More precisely, it has been shown that clutter in high resolution SAR images can be modeled as a compound Gaussian process. In this letter, a modified approach to phase estimation of DS named MPTA is proposed based on the compound Gaussian model. Experiments on real data are presented to demonstrate the effectiveness of the proposed method.

Statistically Homogeneous Pixels (SHPs) selection is a significant step of multi-temporal interferometric synthetic aperture radar (InSAR) for Distributed Scatterers (DS). A series of studies namely, Anderson-Darling test (AD test) and its variants, have demonstrated their advantages. However, these algorithms have a similar drawback that they put little attention on the spatial amplitude distributions and cost too much time of processing. To solve the problem, this paper proposes a modified statistically homogeneous pixels selection algorithm (MoSHPS). It utilizes the amplitude values to get the prior information of the images through an unsupervised classifier, in order to guide the SHPs selection. It can improve the accuracy for SHPs selection, and promote the computing efficiency. In the end, results on a series of real TerraSAR-X datas, acquired over a Tianjin area, confirm the effectiveness of this algorithm.

Statistically homogeneous pixels (SHPs) are considerably significant in many interferometric applications, such as interferometric filtering, distributed scatterer selection, small baseline subset, and SqueeSAR processing. It is very important to achieve SHPs efficiently and accurately. Previous studies on SHPs' selection are based on spatial restrictions and likelihood ratio test, such as Lee filtering, Kolmogorov-Smirnov test, and Anderson-Darling test. However, these algorithms do not stand up to test for the spatial similarity hypothesis for complex terrains with a few images. To solve the problems, this letter proposes a modified SHPs' selection algorithm. It utilizes geometric distance and target features for reaching a priori information to help the similarity hypothesis tests. The proposed algorithm has been tested on simulated and real data to prove the improvements in terms of accuracy and computational efficiency.

InSAR is a technique to extract three-dimensional information on the terrain topography from the phase of the SAR signal and the rational polynomial coefficient (RPC) model has raised considerable interest in the photogrammetry and remote sensing communities. Some work has been done on the RPC model's substitutability for the SAR rigorous sensor model, which is based on the Range-Doppler (RD) equation, moreover, there have been study discussing the substitutability of the RPC model to InSAR (Interferometric SAR) rigorous geometrical sensor model, which is based on the RD equation and phase equation.For the InSAR process, baseline evaluation is very important for the final extracted DEM, but when using RPC model for the replacement of InSAR rigorous geometrical sensor model, the parameter of baseline cannot be included explicitly. Hence, we contend that phase evaluation can be substituted for the baseline evaluation, and it can be applied for RPC model, which can compensate the error caused by orbit and can rebuild the real phase.