Abstract
This paper describes a software implementation of a fast distributed scatterer search algorithm for the problem of displacement velocity calculation based on the Apache Spark platform. A complete scheme for calculating displacement velocities by the persistent scatterer method is considered. The proposed algorithm is integrated into the scheme after the stage of subpixel-accuracy alignment of a stack of time-series images. The search for distributed scatterers is carried out independently in shift windows over the entire area of the image. The presence of distributed scatterers is determined based on the assumption that pairs of samples in the window, which are composed of vectors of complex pixel values in each of the N images, are homogeneous. This assumption stems from the fulfillment of the Kolmogorov–Smirnov criterion for each pair. Toestimate phases of homeogenic pixels, the maximization problem is solved. It is shown that the proposed algorithm is not iterative and can be implemented in the framework of the parallel computing paradigm. Toenable distributed in-memory processing of radar data arrays (from 60 images) across many physical nodes in a network environment, we use the Apache Spark parallel processing platform. In this case, the time it takes to find distributed scatterers is reduced by a factor of 10 on average as compared to a single-processor implementation of the algorithm. The comparative results of testing the computing system on a demo cluster are presented. The algorithm is implemented in Python with a detailed description of the objects and methods of the algorithm.
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The study was funded by the Russian Foundation for Basic Research and Kemerovo region, project no. 20-47-420002 р_.
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Translated by Yu. Kornienko
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Popov, S.E., Potapov, V.P. A Fast Search Algorithm for SqueeSAR Distributed Scatterers in the Problem of Calculating Displacement Velocities. Program Comput Soft 47, 426–438 (2021). https://doi.org/10.1134/S0361768821060062
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DOI: https://doi.org/10.1134/S0361768821060062