# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
## 1 Introduction
In one study [18], the performance of a small baseline subset InSAR (SBAS) was evaluated on a high-performance cluster (HPC) consisting of 16 nodes by analyzing the situation of the different data processing modules of SBAS with different parallelization strategies for data processing, resulting in an overall data processing performance improvement of nearly 16 times in SBAS. Based on the P-SBAS algorithm proposed in the literature [18], a scheme using cloud computing, which is highly scalable, has also been proposed, and a performance evaluation and accuracy validation have been performed using 280 computing nodes of the Amazon Web Services Cloud environment for parallel processing in comparison with the conventional serial processing time, as well as using external GPS measurement data [19; 20; 21; 22; 23]. When the data volume is too large, it is also difficult for the classical PS-InSAR algorithm to obtain the deformation sedimentation rate of the study area in a very short time. The literature [24; 25] proposed a P-CSInSAR parallelization algorithm and performed national deformation mapping, which automatically downloads the study area data using a bash script, and divides the parallelization strategy into image alignment, interference, and time series analysis; three large modules are used to parallelize the PS-InSAR algorithm with different parallelization strategies, which can automatically and efficiently process the Sentinel-1A raw data and greatly improve the data processing performance. Limited by the fact that it generally takes a long time to meet the real-time requirements on a central processing unit (CPU)-based workstation or server, the parallel processing of SAR imaging on a graphics processing unit (GPU) can fully utilize the computational resources of the GPU with the help of the compute unified device architecture (CUDA) [26]. Goldstein filtering is a widely used interferogram filtering algorithm, and the literature [27] proposed a GPU-based parallel filtering processing method, explored the relationship between the filtering time, data size, and filtering window in the parallel processing method, and summarized the advantages and disadvantages of GPU-based parallel computing in filtering. The proposed parallelized processing scheme was able to obtain a 22-fold improvement in the processing time under the guarantee of accuracy. In addition, when the data volume is large or includes many data types, distributed storage and processing through a clustered environment can effectively improve the data storage and processing efficiency [28]. The literature [29]proposed a composite minimum discontinuous phase unwrapping algorithm in a clustered environment, which first divides the original entangled phase into regular chunks based on the main thread and distributes the unwound phase chunks to idle computational nodes for unwinding. Finally, the unwinding results are sent back to the main thread to obtain the final unwound phase, which results in performance improvement. Traditional high-performance computers are mainly designed for \"simple data and complex algorithm\" computing and generally use vertical scaling to obtain more computing power [30]. Distributed technology using parallel computing and the dynamic horizontal scaling of computing nodes can solve the problems of big data storage and processing performance to a significant extent. The cloud platform is constructed by cluster building using the popular big data framework Hadoop [31], and the parallelization of individual processing modules of the SAR data based on Spark [32] or MapReduce [33] can reasonably utilize the computer resources and significantly improve the data processing performance [34; 35; 36; 37; 38]. Most of the current research on parallelization uses multi-file and multi-core parallel computing to parallelize data processing.