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Optimizing the minimum cost flow algorithm for the phase unwrapping process in SAR radar

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The last three decades have been abundant in various solutions to the problem of Phase Unwrapping in a SAR radar. Basically, all the existing techniques of Phase Unwrapping are based on the assumption that it is possible to determine discrete ”derivatives” of the unwrapped phase. In this case a discrete derivative of the unwrapped phase means a phase difference (phase gradient) between the adjacent pixels if the absolute value of this difference is less than π. The unwrapped phase can be reconstructed from these discrete derivatives by adding a constant multiple of 2π. These methods differ in that the above hypothesis may be false in some image points. Therefore, discrete derivatives determining the unwrapped phase will be discontinuous, which means they will not form an irrotational vector field. Methods utilising branch-cuts unwrap the phase by summing up specific discrete partial derivatives of the unwrapped phase along a path. Such an approach enables internally cohesive results to be obtained. Possible summing paths are limited by branch-cuts, which must not be intersected. These branch-cuts connect local discontinuities of discrete partial derivatives. The authors of this paper performed parametrization of the Minimum Cost Flow algorithm by changing the parameter determining the size of a tile, into which the input image is divided, and changing the extent of overlapping of two adjacent tiles. It was the basis for determining the optimum (in terms of minimum Phase Unwrapping time) performance of the Minimum Cost Flow algorithm in the aspect of those parameters.
Rocznik
Strony
511--516
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
  • WB Electronics S.A., 129/133 Poznańska St., 05-850 Ożarów Mazowiecki, Poland, j.dudczyk@wb.com.pl
autor
  • Institute of Radioelectronics, Faculty of Electronics, Military University of Technology, 2 S. Kaliskiego St., 00-908 Warsaw, Poland
Bibliografia
  • [1] P. Berardino, G. Fornaro, R. Lanari, and E. Sansosti, “A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms”, IEEE Trans. Geosci. Remote Sens. 40, 2375–2383 (2002).
  • [2] A. Ferretti, C. Prati, and F. Rocca, “Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry”, IEEE Trans. Geosci. Remote Sens. 38, 2202–2212 (2000).
  • [3] K. Kulpa, M. Malanowski, J. Misiurewicz, and P. Samczynski, “Radar and optical images fusion using stripmap sar data with multilook processing”, Int. J. Electronics and Telecommunications 57, 37–42 (2011).
  • [4] K. Kulpa, “The CLEAN type algorithms for radar signal processing”, IEEE Proc. Microwaves, Radar and Remote Sensing Symposium MRRS’08 1, 152–157 (2008).
  • [5] D.E.C. Zhou and M. Liao, “Application of SAR interferometry on DEM generation of the grove mountains”, Photogramm. Eng. Remote Sens. 70, 1145–1149 (2004).
  • [6] G. Fornaro, A. Pauciullo, and D. Reale, “A null-space method for the phase unwrapping of multitemporal sar interferometric stacks”, IEEE Trans. Geosci. Remote Sens. 49, 2323 -2334 (2011).
  • [7] A. Pepe, L.D. Euillades, M. Manunta, and R. Lanari, “New advances of the extended minimum cost flow phase unwrapping algorithm for SBAS-DInSAR analysis at full spatial resolution”, IEEE Trans. Geosci. Remote Sens. 49, 4062–4079 (2011).
  • [8] C. Chen and H. Zebker, “Network approaches to the twodimensional phase unwrapping: intractability and two new algorithms”, Applied Optics 17, 401–414 (2000).
  • [9] J.M. Huntley, “Noise-immune phase unwrapping algorithm”, Applied Optics 28 (15), 3268–3270 (1989).
  • [10] T. Flynn, “Consistent 2-D phase unwrapping guided by a quality map”, IEEE Proc. Geoscience and Remote Sensing Symposium IGARSS ’96 4, 2057–2059 (1996).
  • [11] M. Costantini, “A novel phase unwrapping method based on network programming”, IEEE Trans. Geosci. Remote Sens. 36, 813–821 (1998).
  • [12] S. Chavez, Q. Xiang, and L. An, “Understanding phase maps in MRI: a new cutline phase unwrapping method”, IEEE Trans. on Medical Imaging 21, 966–977 (2002).
  • [13] S.N. Madsen and H.A. Zebker, “Automated absolute phase retrieval in across-track interferometry”, IEEE Proc. Geoscience and Remote Sensing Symposium IGARSS ’92 2, 1582–1584 (1992).
  • [14] R. Goldstein and H. Zebker, “Topographic mapping from interferometric synthetic aperture radar observations”, J. Geophys. Res. 91 (B5), 4993–4999 (1986).
  • [15] R. Goldstein, H. Zebker, and C. Werner, “Satellite radar interferometry: two-dimensional phase unwrapping”, Radio Science 23, 713–720 (1988).
  • [16] R.K. Ahuja, T.L. Magnanti, and J.B. Orlin, Network Flows: Theory, Algorithms, and Applications, Prentice-Hall, Englewood Cliffs, 1993.
  • [17] D.P. Bertsekas and P. Tseng, “Relaxtion methods for minimum cost ordinary and generalized network flow problems”, Oper. Res. 36, 93–114 (1988).
  • [18] B. Gutmann, “Phase unwrapping with the branch-cut method: clustering of discontinuity sources and reverse simulated annealing”, Applied Optics 38 (26), 5577–5793 (1999).
  • [19] M. Hubig, N. Adam, and S. Suchandt, “MCF-homomorphisms of cost functions for minimum cost flow InSAR phase unwrapping”, IEEE Proc. Geoscience and Remote Sensing Symposium IGARSS ’01 5, 2043–2045 (2001).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4f111544-d65d-44a0-86f0-8ca08f242692
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