Full text of article: Russian language.

 PDF

Abstract:
To detect small movements of the Earth surface (with a velocity of less than several centimeters per year) with use of SAR-interferometry methods it is necessary to find a number of surface areas remaining coherent in radar images over a long period. These areas and corresponding image points are called persistent scatterers. Two methods of persistent scatterers detection are considered in the paper. The methods are compared by the number of detected points and their average time coherence. The algorithms considered are illustrated with an example of processing of a set containing 35 radar images.

Keywords:
SAR interferometry, persistent scatterers, SAR image registration, Harris detector, coherence map.

Citation:
Aduenko AA, Vasileisky AS, Karelov AI, Reyer IA, Rudakov KV, Strijov VV. Algorithms of detection and registration of persistent scatterers in satellite radar images. Computer Optics 2015; 39(4): 622-30. DOI: 10.18287/0134-2452-2015-39-4-622-630.

References:

1. Vasileisky AS, Karatsuba EA, Karelov AI, Kuznetsov MP, Reyer IA. The algorithm of persistent scatterers detection on the satellite radar images of the earth surface [In Russian]. Machine Learning and Data Analysis 2012; 1(4): 473-84.
2. Vasileisky AS, Karatsuba EA, Karelov AI, Kuznetsov MP, Reyer IA. The algorithm of persistent scatterers movement detection on the satellite radar images of the Earth surface [In Russian]. Machine Learning and Data Analysis 2013; 1(5): 489-504.
3. Khashin SI. Dynamic segmentation of frames sequences [In Russian]. Machine Learning and Data Analysis 2013; 1(6): 787-95.
4. Harris C. A combined corner and edge detector / C. Harris, M. Stephens. Alvey Vision Conference 1988; 15: 147-51.
5. Lowe D. Distinctive image features from scale invariant keypoints. IJCV 2004; 60(2): 91-110.
6. Muja M, Lowe DG. Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. International Conference on Computer Vision Theory and Applications (VISAPP'09) 2009; 331-40.
7. Ferretti A, Prati C, Rocca F. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing 2001; 39(1): 8-20.
8. Ferretti A, Prati C, Rocca F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing 2000; 38(5): 2202-12.
9. Prati C, Ferretti A, Perissin D. Recent advances on surface ground deformation measurement by means of repeated space-borne SAR observations. Journal of Geodynamics 2010; 49(3): 161-70.
10. Hetland EA, Musé P, Simons M, Lin YN, Agram PS, DiCaprio CJ. Multiscale InSAR time series (MInTS) analysis of surface deformation. Journal of Geophysical Research: Solid Earth (1978–2012) 2012; 117(B2).
11. Nitti DO, Bovenga F, Nutricato R, Intini F, Chiaradia MT. On the use of COSMO/SkyMed data and Weather Models for interferometric DEM generation. European Journal of Remote Sensing 2013; 46: 250-71.
12. Bamler R, Hartl P. Synthetic aperture radar interferometry. Inverse problems 1998; 14(4): 1-54.
13. Balzter H, Rowland CS, Saich P. Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry. Remote Sensing of Environment 2007; 108(3): 224-39.
14. Digital elevation data. Source: <http://www.viewfinderpa­noramas.org/dem3.html#3dem>. Access date: 23.01.2014.
15. Vorontsov KV, Frey AI, Sokolov EA. Computable Combinatorial Overfitting Bounds. Machine Learning and Data Analysis 2013; 1(6): 734-43.
16. Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Butbank D, Alsdorf D. The Shuttle Radar Topography Mission. Reviews of Geophysics 2007; 45.

---

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail:journal@computeroptics.ru; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20