Fast InSAR multichannel phase unwrapping for DEM generation

In this paper, a method to solve the multichannel phase unwrapping problem is presented. MAP approach together with Markov Random Fields have proved to be effective, allowing to restore the uniqueness of the solution without introducing external constraints to regularize the problem. The idea is to develop a fast algorithm to unwrap the interferometric phase in the multichannel configuration, which is, in the main time, able to provide the global optimum solution. To reach this target, an a priori model based on Total Variation is used together with optimization algorithm based on graph-cut technique. The proposed approach has been tested both on simulated and real data. The obtained results show the effectiveness of our approach.

[1]  Jérôme Darbon,et al.  Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization , 2006, Journal of Mathematical Imaging and Vision.

[2]  Vito Pascazio,et al.  Maximum a posteriori estimation of height profiles in InSAR imaging , 2004, IEEE Geoscience and Remote Sensing Letters.

[3]  Toke Koldborg Jensen,et al.  An adaptive pruning algorithm for the discrete L-curve criterion , 2007 .

[4]  S. Gómez,et al.  The triangle method for finding the corner of the L-curve , 2002 .

[5]  David K. Smith.  Network Flows: Theory, Algorithms, and Applications , 1994 .

[6]  R. Bamler,et al.  Synthetic aperture radar interferometry , 1998 .

[7]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[8]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[9]  Jérôme Darbon,et al.  Image Restoration with Discrete Constrained Total Variation Part II: Levelable Functions, Convex Priors and Non-Convex Cases , 2006, Journal of Mathematical Imaging and Vision.

[10]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Sergei V. Pereverzev,et al.  Morozov's discrepancy principle for tikhonov , 2000 .

[12]  D. R. Fulkerson,et al.  Maximal Flow Through a Network , 1956 .

[13]  Dianne P. O'Leary,et al.  The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems , 1993, SIAM J. Sci. Comput..

[14]  M. Rudnicki,et al.  The Use of L-Curve and U-Curve in Inverse Electromagnetic Modelling , 2008 .

[15]  Fuk K. Li,et al.  Synthetic aperture radar interferometry , 2000, Proceedings of the IEEE.

[16]  Vito Pascazio,et al.  Multifrequency InSAR height reconstruction through maximum likelihood estimation of local planes parameters , 2002, IEEE Trans. Image Process..

[17]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[18]  Dennis C. Ghiglia,et al.  Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software , 1998 .

[19]  Hiroshi Ishikawa,et al.  Exact Optimization for Markov Random Fields with Convex Priors , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  José M. Bioucas-Dias,et al.  Phase Unwrapping via Graph Cuts , 2007, IEEE Trans. Image Process..

[21]  Jérôme Darbon,et al.  Joint Filtering of SAR Interferometric and Amplitude Data in Urban Areas by TV Minimization , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[22]  Jérôme Darbon,et al.  JOINT FILTERING OF SAR INTERFEROMETRIC PHASE AND AMPLITUDE DATA IN URBAN AREAS BY TV MINIMIZATION , 2008 .

[23]  Stan Z. Li,et al.  Markov Random Field Modeling in Image Analysis , 2001, Computer Science Workbench.