2017-06-27
Characteristic Analysis of Phase Glint in InSAR Image Processing
By
Progress In Electromagnetics Research M, Vol. 58, 43-55, 2017
Abstract
This paper investigates the phase glint problem involved in interferometric synthetic aperture radar (InSAR) image processing, which refers to the multiple scatterer interference of a single pixel, and studies the distribution of interferometric phase in the case of double scatterer interference. It is found that the value range of the observed interferometric phase is related to several factors including the complex scattering coefficient ratio and interferometric phase difference between the elementary scatterers, and no matter what values of interferometric phases of elementary scatterers are taken, the dynamic range of interferometric phase of phase glintis always. This paper also briefly analyzes the impact of phase glint on classical InSAR image processing and man-made target height retrieval, and it is concluded that the phase glint will induce significant height estimating error. Simulation and real data results verify the conclusion.
Citation
Jing-Ke Zhang, Dahai Dai, Zong-Feng Qi, Yong-Hu Zeng, and Liandong Wang, "Characteristic Analysis of Phase Glint in InSAR Image Processing," Progress In Electromagnetics Research M, Vol. 58, 43-55, 2017.
doi:10.2528/PIERM17031601
References

1. Cumming, I. G. and F. K. Wong, Digital Processing of Synthetic Aperture Radar Data: Algorithm and Implementation, Artech House, 2005.

2. Henke, D., C. Magnard, M. Frioud, et al. "Moving-target tracking in single-channel wide-beam SAR," IEEE Trans. on Geosci. Remote Sens., Vol. 50, No. 11, 4735-4747, 2012.
doi:10.1109/TGRS.2012.2191561        Google Scholar

3. Mouche, A. A., F. Collard, B.Chapron, et al. "On the use of doppler shift for sea surface wind retrieval from SAR," IEEE Trans. on Geosci. Remote Sens., Vol. 50, No. 7, 2901-2909, 2012.
doi:10.1109/TGRS.2011.2174998        Google Scholar

4. Zhou, J. X., Z. G. Shi, X. Cheng, et al. "Automatic target recognition of SAR imagesbased on global scattering center model," IEEE Trans. on Geosci. Remote Sens., 3713-3729, 2011.        Google Scholar

5. Papson, S. and R. M. Narayanan, "Classification via the shadow region in SAR imagery," IEEE Trans. on Aerospace and Electronic Systems, Vol. 48, No. 2, 969-980, 2012.
doi:10.1109/TAES.2012.6178042        Google Scholar

6. Dabboor, M., M. J. Collins, V. Krrathanassi, et al. "An unsupervised classification approach for polarimetric SAR data based on the chernoff distance for complex wishart distribution," IEEE Trans. Geosci. Remote Sens., Vol. 51, No. 7, 4200-4213, 2013.
doi:10.1109/TGRS.2012.2227755        Google Scholar

7. Zhu, X. X. and R. Bamler, "Tomographic SAR inversion by L1-norm regularization - The Compressive Sensing Approach," IEEE Trans. Geosci. Remote Sens., Vol. 48, No. 10, 3839-3846, 2010.
doi:10.1109/TGRS.2010.2048117        Google Scholar

8. Xing, S. Q., Y. Z. Li, D. H. Dai, et al. "Three-dimensional reconstruction of man-made objects using polarimetric tomographic SAR," IEEE Trans. Geosci. Remote Sens., Vol. 51, No. 6, 3694-3705, 2013.
doi:10.1109/TGRS.2012.2220145        Google Scholar

9. Rosen, P. A., S. Hensley, I. R. Joughin, et al. "Synthetic aperture radar interferometry," Proc. IEEE, Vol. 88, No. 3, 333-382, 2000.
doi:10.1109/5.838084        Google Scholar

10. Berardino, P., G. Fornaro, R. Lanari, et al. "A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms," IEEE Trans. Geosci. Remote Sens., Vol. 40, No. 11, 2375-2383, 2002.
doi:10.1109/TGRS.2002.803792        Google Scholar

11. Cloude, S. R. and K. P. Papathanassiou, "Polarimetric SAR interferometry," IEEE Trans. Geosci. Remote Sens., Vol. 36, No. 5, 1551-1565, 1998.
doi:10.1109/36.718859        Google Scholar

12. Papathanassiou, K. P. and S. R. Cloude, "Single baseline polarimetric SAR interferometry," IEEE Trans. Geosci. Remote Sens., Vol. 39, No. 11, 2352-2363, 2001.
doi:10.1109/36.964971        Google Scholar

13. Zhu, X. X. and R. Bamler, "Demonstration of super-resolution for tomographic SAR imaging in urban environment," IEEE Trans. Geosci. Remote Sens., Vol. 50, No. 8, 3150-3157, 2012.
doi:10.1109/TGRS.2011.2177843        Google Scholar

14. Austin, C. D. and R. L. Moses, "IFSAr processing for 3D target reconstruction," Algorithms for Synthetic Aperture Radar Imagery XII, SPIE Defense and Security Symposium, Orlando, 2005.        Google Scholar

15. Austin, C. D. and R. L. Moses, "Interferometric synthetic aperture radar detection and estimation based 3D image reconstruction," Algorithms for Synthetic Aperture Radar Imagery XIII, SPIE Defense and Security Symposium, Orlando, 2006.        Google Scholar

16. Xing, S. Q., "Study on the 3D imaging of manmade target based on polarimetric radar," China National University of Defense Technology, 2013.        Google Scholar

17. Pauciullo, A., D. Reale, A. D. Maio, et al. "Detection of double scatterers in SAR tomography," IEEE Trans. Geosci. Remote Sens., Vol. 50, No. 9, 3567-3586, 2012.
doi:10.1109/TGRS.2012.2183002        Google Scholar

18. Lombardini, F. and M. Pardini, "Superresolution differential tomography: Experiments on identification of multiple scatterers in spaceborne SAR data," IEEE Trans. Geosci. Remote Sens., Vol. 50, No. 4, 1117-1129, 2012.
doi:10.1109/TGRS.2011.2164925        Google Scholar

19. Burrows, M. L., "Two-dimensional ESPRIT with tracking for radar imaging and feature extraction," IEEE Trans. Antenna Propagat., Vol. 52, No. 2, 524-532, 2004.
doi:10.1109/TAP.2003.822411        Google Scholar