# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
## 1 Introduction  
Since the permanent scatterer (PS) interferometry technique [1] was first proposed in 2000, various multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been developed [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. The classical PS-InSAR technique obtains the deformation information of the study area by constructing a triangular network, by selecting PSs that are not affected by space-time and using the amplitude discretization method to measure the spatio-temporal stability of the point target intensity. However, in non-urban areas, such as vegetation, bare land, and low-intensity impermeable surfaces, it is often difficult to obtain accurate deformation information, owing to the limitations of the algorithm. In response to the above problem, [PERSON] et al. proposed the SqueezeSAR technique [13], which considers the different statistical forms of PSs and distributed scatterers (DSs) and introduces a new way to jointly process PSs and DSs. DSs are susceptible to time and space decorrelation factors, which makes it difficult for interferograms to meet the requirements of time-series analysis. Therefore, before deformation settlement, the DS point targets must be screened and optimized to improve the observed signal-to-noise ratio and reduce the error propagation probability [14; 15; 16]. In contrast to the conventional MT-InSAR, SqueezeSAR uses the Kolmogorov-Smirnov (KS) test [17] to find the DS point targets with the same statistical characteristics in a given range, and then acquires the surface deformation through the spatially adaptive filtering of these DS point targets. Finally, it reconstructs the phase using the phase triangle algorithm(PTA), employing the data processing of PS-InSAR. Therefore, the core steps of SqueeSAR include the homogeneous distribution test algorithm based on the KS test, spatial adaptive filtering, and the phase triangulation algorithm. It was found that the SqueeSAR algorithm is limited in terms of spatial adaptive filtering because of the need to generate N(N\(-1\))/2 pairs of interferograms for phase reconstruction to obtain a better accuracy of surface deformation. On one hand, considering that the amount of data of a single interferogram can reach hundreds or even thousands of MB (depending on the size of the study area) and that spatial adaptive filtering requires the phase information of N(N\(-1\))/2 interferograms to be read simultaneously, this leads to large memory consumption or even memory overflow in the process of loading the interferogram phase information. On the other hand, during the entire filtering process, each statistically homogeneous pixel (SHP) identified by the KS test must be filtered, which results in a significant processing time. Spatial adaptive filtering plays an indispensable role in the SqueeSAR algorithm, which mainly averages the phase information generated from the SHP based on the N(N\(-1\))/2 interferograms that are spatially identified by the KS test algorithm to reduce the speckle noise in the averaging area (farmland, forest, etc.) without affecting the phase value of the point object target; thus, the phase triangulation algorithm can invert the performance with good accuracy. Considering the current problems, such as the dramatic increase in computer compliance and complexity in data processing due to the massive amount of data brought about by the launch of satellites with SAR sensors, there is an urgent need for a solution for SAR data processing to alleviate problems such as low data processing performance and hardware limitations. To relieve the pressure of data storage and reduce the data processing time in order to obtain the deformation rate results of the study area in a short period of time, a clustered environment with disk expansion to store data and a parallelization strategy to process SAR data is the solution chosen by most researchers.