Skip to main content
Log in

A Fast Search Algorithm for SqueeSAR Distributed Scatterers in the Problem of Calculating Displacement Velocities

  • Published:
Programming and Computer Software Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.
Fig. 14.

Similar content being viewed by others

REFERENCES

  1. Ferretti, A., Prati, C., Rocca, F., and Wasowski, J., Satellite interferometry for monitoring ground deformations in the urban environment, Proc. 10th Congr. Int. Association for Engineering Geology and the Environment (IAEG), 2006, pp. 1–4.

  2. Crosetto, M., Monserrat, O., Cuevas-Gonzalez, M., Devanthery, N., and Crippa, B., Persistent scatterer interferometry: A review, ISPRS J. Photogramm. Remote Sens., 2016, vol. 115, pp. 78–89.

    Article  Google Scholar 

  3. Perissin, D. and Ferretti, A., Urban target recognition by means of repeated spaceborne SAR images, IEEE Trans. Geosci. Remote Sens., 2007, vol. 45, no. 12, pp. 4043–4058.

    Article  Google Scholar 

  4. Hooper, A., A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches, Geophys. Res. Lett., 2008, vol. 35, p. 302.

    Article  Google Scholar 

  5. Rocca, F., Modeling interferogram stacks, IEEE Trans. Geosci. Remote Sens., 2007, vol. 45, no. 10, pp. 3289–3299.

    Article  Google Scholar 

  6. Zebker, H.A. and Shanker, A.P., Geodetic imaging with time series persistent scatterer InSAR, American Geophysical Union, 2008.

    Google Scholar 

  7. Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., and Rucci, A., Exploitation of distributed scatterers in interferometric data stacks, Proc. Int. Geoscience Remote Sensing Symp. (IGARSS), Cape Town, 2009.

  8. Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., and Rucci, A., The second generation PSInSAR approach: SqueeSAR, Proc. Int. Workshop ERS SAR Interferometry (FRINGE), Frascati, Italy, 2009.

  9. Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., and Rucci, A., A new algorithm for processing interferometric data-stacks: SqueeSAR, IEEE Trans. Geosci. Remote Sens., 2011, vol. 49, pp. 3460–3470.

    Article  Google Scholar 

  10. Apache Spark documentation. https://spark.apache.org/docs/latest/index.html Accessed September 12, 2020.

  11. Feoktistov, A.A., Zakharov, A.I., Denisov, P.V., and Gusev, M.A., Investigating the dependence of the RM radar data processing results on the processing parameters, Part 4: Main directions of development of the persistent scatterer method; key points of the SQUEESAR and STAMPS methods, Zh. Radioelektron., 2017, no. 7. http://jre.cplire.ru/jre/jul17/5/text.pdf

  12. Cao, N., Lee, H., and Chul Jung, H., Mathematical framework for phase-triangulation algorithms in distributed-scatterer interferometry, IEEE Geosci. Remote Sens. Lett., vol. 12, no. 9, pp. 1838–1842.

  13. Guarnieri, A.M. and Tebaldini, S., On the exploitation of target statistics for SAR interferometry applications, IEEE Trans. Geosci. Remote Sens., 2008, vol. 46, no. 11, pp. 3436–3443.

    Article  Google Scholar 

  14. Bamlery, R. and Hartlz, P., Synthetic aperture radar interferometry, Inverse Probl., 1998, vol. 14, no. 4, pp. R1–R54.

    Article  MathSciNet  Google Scholar 

  15. Shamshiri, R., Nahavandchi, H., Motagh, M., and 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 Sens., 2018, vol. 10, no. 5, pp. 794–808.

    Article  Google Scholar 

  16. STAMPS, A software package to extract ground displacements from time series of synthetic aperture radar (SAR) acquisitions. https://homepages.see.leeds.ac.uk/~earahoo/stamps Accessed September 12, 2020.

  17. Potapov, V.P., Popov, S.E., and Kostylev, M.A., Information and computing system for massively parallel processing of radar data in the Apache Spark environment, Vychisl. Tekhnol., 2018, vol. 23, no. 4, pp. 110–123.

    Google Scholar 

  18. Popov, S.E., Zamaraev, R.Yu., and Mikov, L.S., Massively parallel approach to radar data processing, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2020, vol. 17, no. 2, pp. 49–61.

    Article  Google Scholar 

Download references

Funding

The study was funded by the Russian Foundation for Basic Research and Kemerovo region, project no. 20-47-420002 р_.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to S. E. Popov or V. P. Potapov.

Additional information

Translated by Yu. Kornienko

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0361768821060062

Navigation