Abstract.

An effective control of geodynamic processes using multiple radar satellite survey and differential interferometric processing of received data requires the identification of terrain areas that preserve an acceptable level of coherence on radar images over a long period. Analysis of the phase component of the images for such areas, called persistent scatterers, makes it possible to estimate the values of small displacements of the observed surface with velocities less than several centimeters per year. In this paper, two radar differential interferometry methods based on the identification of persistent scatterers are considered: the standard method of persistent scatterers and the proposed modification of the method based on the use of persistent scatterer pairs. For both methods it is suggested not to perform a direct phase unwrapping, which is most difficult when most known methods are used. For the method of persistent scatterer pairs it is suggested to apply the quadratic penalty not for the phase unwrapping, but at the final processing stage to recover the absolute values of displacements and corrections of an a priori elevation model from the obtained relative values. The application of the algorithms considered is illustrated by the processing of an interferometric series of 35 radar images obtained by the COSMO-SkyMed system.

Keywords:

SAR interferometry, persistent scatterers, method of persistent scatterer pairs, digital elevation model, phase unwrapping

PP. 29-43.

References

1. Gornyy, V.I., and A.A. Tronin. 2012. Obzor dostizheniy poslednego desyatiletiya v oblasti primeneniya sputnikovykh metodov distantsionnogo zondirovaniya pri geologicheskikh i geofizicheskikh issledovaniyakh [Review of the last decade major achievements of remote sensing methods application on the geological and geophysical problems solution]. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa [Current problems in remote sensing of the Earth from space]. 9(5):116–132.
2. Bamler, R., and P. Hartl. 1998. Synthetic aperture radar interferometry. Inverse problems. 14(4):1–54.
3. Hetland, E.A., P. Musé, M. Simons, Y.N. Lin, P.S. Agram, and C.J. DiCapri. 2012. Multiscale InSAR time series (MInTS) analysis of surface deformation. Journal of Geophysical Research: Solid Earth (1978-2012). 117(B2). Available at:
http://onlinelibrary.wiley.com/doi/10.1029/2011JB008731/full (accessed January 22, 2018).
4. Mikhailov, V.O., E.A. Kiseleva, E.I. Smolyaninova, V.I. Golubev, P.N. Dmitriev, E.P. Timoshkina, and S.A. Khairetdinov. 2016. Obobshchenie opyta primeneniya razlichnykh metodov obrabotki RSA snimkov dlya izucheniya i monitoringa opolznevoy aktivnosti sklonov v rayone Bol’shogo Sochi [Review of the results of application of different methods for SAR image processing to study and monitor landslide activity in the Big Sochi region]. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa [Current problems in remote sensing of the Earth from space] 13(6):137–147.
5. Cigna, F., F. Bianchini, and N. Casagli. 2013. How to assess landslide activity and intensity with Persistent Scatterer Interferometry (PSI): the PSI-based matrix approach. Landslides. (10):267–283.
6. Gornyy, V.I., S.G. Kritsuk, I.S. Latypov, A.G. Olovyannyy, S.D. Petrov, and A.A. Tronin. 2014. O mekhanizme znakoperemennykh vertical’nykh dvizheniy poverkhnosti gorodskoy sredy (po rezul’tatam sputnikovoy radiolokatsionnoy interferometrii) [On the mechanism of land surface vertical oscillations in urban areas (by satellite radar interferometry)]. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa [Current problems in remote sensing of the Earth from space]. 11(3):129–139.
7. Filatov, A.V., A.V. Yevtyushkin, and Y.V. Vasiliev. 2012. Mnogoletniy geodinamicheskiy monitoring neftegazovykh mestorozhdeniy Zapadnoy Sibiri metodom sputnikovoy radiolokatsionnoy interferometrii [Long-term geodynamic monitoring of oil and gas fields in Western Syberia by InSar technique]. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa [Current problems in remote sensing of the Earth from space]. 9(2):39–47.
8. Rosenberg, I.N., E.A. Lupyan, M.M. Zheleznov, and A.S. Vasileisky. 2015. Vozmozhnosti ispol’zovaniya sputnikovykh tekhnologiy dlya monitoringa zheleznodorozhnoy infrastruktury [The potentialities of satellite technologies usage for railway infrastructure monitoring]. Renessans zheleznykh dorog: fundamental’nye nauchnye issledovaniya i proryvnye innovatsii [Railway Renaissance: fundamental research and outbreaking innovations]. Noginsk: Analitica Rodis. 97–112.
9. Zaichko, V.A., and V.A. Selin. 2013. Meropriyatiya Federal’nogo kosmicheskogo agentstva Rossii po sozdaniyu kosmicheskikh sredstv radiolokatsionnogo nablyudeniya i tekhnologiy kompeksnoy obrabotki dannykh [Russian Federal Space Agency activities for the creation of the space radar observation facilities and integrated data processing technologies]. Vestnik SibGAU [Herald of Siberian State Aerospace University (SibGAU)]. (5):4–5.
10. Nitti D.O., F. Bovenga, R. Nutricato, F. Intini, and M.T. Chiaradia. 2013. On the use of COSMO/SkyMed data and Weather Models for interferometric DEM generation. European Journal of Remote Sensing. 46:250–271.
11. Ferretti, A., C.Prati, and F.Rocca. 2000. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE transactions on Geoscience and Remote Sensing 38(5):2202–2212.
12. Ferretti, A., C.Prati, and F.Rocca. 2001. Permanent scatterers in SAR interferometry. IEEE transactions on Geoscience and Remote Sensing. 39(1):8–20.
13. Costantini, M., S. Falco, F. Malvarosa, and F. Minati. 2008. A new method for identification and analysis of persistent scatterers in series of SAR images. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008. 2:449–452.
14. Vorontsov, K.V., and A.A. Potapenko. 2013. Modifikatsii EM-algoritma dlya veroyatnostnogo tematicheskogo modelirovaniya [EM-like algorithms for probabilistic topic modeling]. Mashinnoe Obuchenie i Analiz Dannykh [Machine Learning and Data Analysis]. 1(6):657–686.
15. Aduenko, A.A., A.S. Vasileisky, A.I. Karelov, I.A. Reyer, K.V. Rudakov, and V.V. Strijov. 2015. Algoritmy vydeleniya i sovmeshcheniya ustoychivykh otrazhateley na sputnikovykh snimkakh [Algorithms of detection and registration of persistent scatterers in satellite radar images]. Komp'uternaya Optika [Computer Optics]. 39(4):622–630. doi:10.18287/0134-2452-2015-39-4-622-630.
16. Harris C., and M. Stephens. 1988. A combined corner and edge detector. Proceedings of the Fourth Alvey Vision Conference. 15:147–151.
17. Lowe D. 2004. Distinctive image features from scale invariant keypoints. IJCV. 60(2):91–110.
18. Muja M., and D.G. Lowe. 2009. Fast approximate nearest neighbors with automatic algorithm configuration. Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP’09). 331–340.
19. Prati C., A. Ferretti , and D. Perissin. 2010. Recent advances on surface ground deformation measurement by means of repeated space-borne SAR observations. Journal of Geodynamics. 49(3):161–170.
20. Farr T.G., P.A. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, M. Paller, E. Rodriguez, L. Roth, D. Seal, S. Shaffer, J. Shimada, J. Umland, M. Werner, M. Oskin, D. Butbank, and D. Alsdorf. 2007. The Shuttle Radar Topography Mission. Reviews of Geophysics. 45. doi:10.1029/2005RG000183