|
Article information
2021 , Volume 26, ¹ 4, p.82-97
Popov S.E., Potapov V.P., Zamaraev R.Y.
Parallel algorithm for the identification of distributed scatterers in the problem of calculating the velocities of displacements of the earth's surface by the Persistent Scaterrers method
The article describ es implementation of the software for a fast algorithm which finds distributed scatterers for the problem of plotting displacement velocities of the earth’s surface based on the Apache Spark platform. The Persistent Scatterer (PS) method is widely used for estimating the displacement rates of the earth’s surface. It consists of the identification of coherent radar targets (interferogram pixels) that demonstrate high phase stability during the entire observation period. The most advanced algorithm for solving the identification problem is the SqueeSAR algorithm. It allows searching and processing Distributed Scatterers (DS) — specific reflectors, integrating them into the general scheme for calculating displacement velocities using the PS method. A careful analysis of the SqueeSAR algorithm has identified areas that are critical to its performance. The whole algorithm is based on an enumeration of the initial data, where nontrivial transformations are performed at each step. The stages of searching for adjacent points in the design window with multiple passes over the entire area of the image and solving the maximization problem when assessing the real values of the interferometric phases turned out to be noticeably costly. To speed up the processing of images, it is proposed to use the Apache Spark massively parallel computing platform. Sp ecialized primitives (Resilient Distributed Data) for recurrent in-memory processing are available here. This provides multiple accesses to the radar data loaded into memory from each cluster node and allows logical dividing of the snapshot stack into subareas. Thus calculations are performed indep endently in massively parallel mode. Based on the SqueeSAR mathematical model, it is assumed that the radar image data and the calculated geophysical parameters calculated are common for each statistically homogeneous sample of nearby pixels. In accordance with this assumption, the uniformity (homogeneity) of the pixels is estimated within a given window. The search for distributed scatterers occurs indep endently by the sequence of shifts of the windows over the entire area of the image. The window is shifted along the width and height of the image with a step equal to the width and height of the window. Pairs of samples in the window are composed of vectors of complex pixel values in each of the 𝑁 images. The validity of the Kolmogorov – Smirnov criterion is checked for each of the pairs. To estimate the values of the phases of homogeneous pixels, the maximization problem is solved. The method of maximum likelihood estimation (MLE) is considered. The construction of the correct MLE form is carried out by analyzing the statistical properties of the coherence matrix of all images using the complex Wishart distribution. The Apache Spark platform applied here permits processing of distributed radar data stack arrays in memory on a large number of physical nodes in a network environment. The average search time for distributed scatterers turned out to be 10 times less compared to the uniprocessor implementation of the algorithm. The algorithm is implemented in the Python programming language with a detailed description of the objects and methods of the algorithm. The proposed algorithm and its parallel implementation allows applying the developed approaches to other problems and types of satellite data for remote sensing of the earth from space.
[full text]
Keywords: differential interferometry, ground displacements, massively parallel computing, data analysis
doi: 10.25743/ICT.2021.26.4.008
Author(s):
Popov Semen Evgenievich
PhD.
Position: Senior Research Scientist
Office: Federal Research Center for Information and Computational Technologies
Address: 630090, Russia, Novosibirsk, Lavrentiev avenue, 6
Phone Office: (905)9692107
E-mail: popov@ict.sbras.ru
SPIN-code: 5627-9584Potapov Vadim Petrovich
Dr. , Professor
Position: Leading research officer
Office: Federal Research Center for Information and Computational Technologies
Address: 650003, Russia, Kemerovo, Lavrentiev avenue, 6
Phone Office: (3842) 21-14-00
E-mail: vadimptpv@gmail.com
SPIN-code: 8947-1880Zamaraev Roman Yurjevich
PhD.
Position: Senior Research Scientist
Office: Federal Research Center for Information and Computational Technologies
Address: 630090, Russia, Novosibirsk, Academician M.A. Lavrentiev avenue, 6
Phone Office: (905)0692866
E-mail: zamaraev@ict.sbras.ru
SPIN-code: 6583-5920 References:
1. Ferretti A., Prati C., Rocca F., Wasowski J. Satellite interferometry for monitoring ground deformations in the urban environment. Proceedings of the 10th Congress of the International Association for Engineering Geology and the Environment (IAEG). United Kingdom: Nottingham; 2006: 1–4.
2. Crosetto M., Monserrat O., Cuevas-Gonzalez M., Devanthery N., Crippa B. Persistent scatterer interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 2016; (115):78–89.
3. Perissin D., Ferretti A. Urban target recognition by means of repeated spaceborne SAR images. IEEE Transactions on Geoscience and Remote Sensing. 2007; 45(12):4043–4058.
4. Hooper A. A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophysics Research Letter. 2008; 35(16):1–5.
5. Rocca F. Modeling interferogram stacks. IEEE Transactions on Geoscience and Remote Sensing. 2007; 45(10):3289–3299.
6. Zebker H.A., Shanker A.P. Geodetic imaging with time series persistent scatterer InSAR. Available at: https://www.researchgate.net/publication/252083714_Geodetic_imaging_with_time_ series_persistent_scatterer_InSAR.
7. Chaabane F., Sellami M., Marie J.N., Tupin F. InSAR permanent scatterers selection using SAR SVA filtering. Available at: https://ieeexplore.ieee.org/document/5417746.
8. Ferretti A., Novali F. Beyond PSInSAR: The SQUEESAR approach. Available at: https://www.researchgate.net/publication/252209721_Beyond_PSInSAR_the_SQUEESAR_Approach.
9. Ferretti A., Fumagalli A., Novali F., Prati C., Rocca F., Rucci A. A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Transactions on Geoscience and Remote Sensing. 2011; (49):3460–3470.
10. Apache Spark overview — Spark 3.1.2 documentation. Available at: https://spark.apache.org/ docs/latest/index.html (accessed: 12.09.2020).
11. Feoktistov A.A, Zahkarov A.I., Denisov P.V, Gusev M.A. Investigation of the dependence of the processing results of the remote sensing radar data on the processing parameters. Part 4. Main directions for the development of the method of constant scatterers. Available at: http://jre. cplire.ru/jre/jul17/5/text.pdf. (In Russ.)
12. Cao N., Lee H., Chul Jung H. Mathematical framework for phase-triangulation algorithms in distributed-scatterer interferometry. IEEE Geoscience and Remote Sensing Letters. 2015; 12(9):1838–1842.
13. Guarnieri A.M., Tebaldini S. On the exploitation of target statistics for SAR interferometry applications. IEEE Transactions on Geoscience and Remote Sensing. 2008; 46(11):3436–3443.
14. Bamlery R., Hartlz P. Synthetic aperture radar interferometry. Inverse Problems. 1998; 14(4):R1–R54.
15. Shamshiri R., Nahavandchi H., Motagh M., Hooper A. Efficient ground surface displacement monitoring using Sentinel-1 data: Integrating distributed scatterers (DS) identified using two-sample t-test with persistent scatterers (PS). Remote Sensing. 2018; 10(5):794–808.
16. STAMPS. A software package to extract ground displacements from time series of synthetic apertureradar (SAR) acquisitions. Available at: https://homepages.see.leeds.ac.uk/earahoo/stamps (accessed 12.09.2020).
17. Potapov V.P., Popov C.E., Kostylev M.A. The information and computational system for the massive parallel processing of radar data based on Apache Spark framework. Computational Technologies. 2018; 23(4):110–123. DOI:10.25743/ICT.2018.23.16507. (In Russ.)
18. Popov S.E., Zamaraev R.Yu., Mikov L.S. Mass-parallel approach to radar data processing.
Bibliography link:
Popov S.E., Potapov V.P., Zamaraev R.Y. Parallel algorithm for the identification of distributed scatterers in the problem of calculating the velocities of displacements of the earth's surface by the Persistent Scaterrers method // Computational technologies. 2021. V. 26. ¹ 4. P. 82-97
|
