Designing and implementation of variance-dependent Goldstein radar interferogram filtering

Qi-jie Wang , Xiao-hu Zhang , Jian-jun Zhu , Jun Hu

Journal of Central South University ›› 2014, Vol. 21 ›› Issue (8) : 3295 -3301.

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Journal of Central South University ›› 2014, Vol. 21 ›› Issue (8) : 3295 -3301. DOI: 10.1007/s11771-014-2302-z
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Designing and implementation of variance-dependent Goldstein radar interferogram filtering

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Abstract

The variance-dependent Goldstein radar interferogram filter takes into account the information of both interferometric coherence and multilook factors, and can produce very consistent results for interferograms generated under a wide variety of multilook factors and with very different noise level. However, the filter is a bit complicated and its application is still very limited. We present the designing and implementation of the variance-dependent Goldstein radar interferogram filtering, emphasizing on the logic flow, the generation of look-up table, the determination of filtering parameter, and the handling of edge information loss. Experiments with real interferograms are provided to demonstrate the applications of the designed filtering. Comparisons with the result of the coherence-dependent Goldstein filter show that improvements from 18.4% to 36.9% are achieved when the variance-dependent filter is used, and the noisier the interferogram, the greater the improvement.

Keywords

synthetic aperture radar interferometry (InSAR) / Goldstein filter / variance-dependent radar / coherence / multilook factor

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Qi-jie Wang, Xiao-hu Zhang, Jian-jun Zhu, Jun Hu. Designing and implementation of variance-dependent Goldstein radar interferogram filtering. Journal of Central South University, 2014, 21(8): 3295-3301 DOI:10.1007/s11771-014-2302-z

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References

[1]

MadsenS N, ZebkerH A, MartinJ. Topographic mapping using radar interferometry: Processing techniques [J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(1): 246-256

[2]

YanY J, TrouveE, PinelV, MaurisG, PathierE, GalichetS. Fusion of D-InSAR and sub-pixel image correlation measurements for coseismic displacement field estimation: Application to the Kashmir earthquake (2005) [J]. International Journal of Image and Data Fusion, 2012, 3(1): 71-92

[3]

DingX L, LiuG X, LiZ W, LiZ L, ChenY Q. Ground subsidence monitoring in Hong Kong with satellite SAR interferometry [J]. Photogrammetric Engineering and Remote Sensing, 2014, 70(10): 1151-1156

[4]

HuJ, LiZ W, ZhangL, DingX L, ZhuJ, SunQ, DingW. Correcting ionospheric effects and monitoring two-dimensional displacement fields with multiple-aperture InSAR technology with application to the Yushu earthquake [J]. Science China Earth Sciences, 2012, 55(12): 1961-1971

[5]

XuW B, LiZ W, DingX L, WangC C, FengG C. Application of small baseline subsets D-InSAR technology to estimate the time series land deformation and aquifer storage coefficients of Los Angeles area [J]. Chinese J Geophys, 2012, 55(2): 452-461

[6]

Lij, LiZ-w, ZhuJ-j, DingX-l, WangC-cheng. Deriving surface motion of mountain glaciers in the Tuomuer-Khan Tengri mountain ranges from PLASAR images [J]. Global and Planetary Changes, 2013, 101: 61-71

[7]

WangQ-j, LiZ-w, DuY-n, XieR-a, ZhangX-q, JiangM, ZhuJ-jun. Generalized functional model of the maximum and minimum detectable deformation gradient for PALSAR Interferometry [J]. Transactions of Nonferrous Metals Society of China, 2014, 24(3): 824-832

[8]

ZebkerH A, VillasenorJ. Decorrelation in interferometric radar echoes [J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(5): 950-959

[9]

LiZ W, DingX L, HuangC, ZouZ R. Atmospheric effects on repeat-pass InSAR measurements over Shanghai region [J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2007, 69: 1344-1356

[10]

SunQ, LiZ-w, ZhuJ-j, DingX-l, HuJ, XuBing. Improved Goldstein filter for InSAR noise reduction based on local SNR [J]. Journal of Central South University, 2013, 20(7): 1896-1903

[11]

LiZ W, DingX L, HuangC, ZhengD W. Filtering method for radar interferogram with strong noise [J]. International Journal of Remote Sensing, 2006, 27(14): 2991-3000

[12]

GoldsteinR M, WenerC L. Radar interferogram filtering for geophysical applications [J]. Geophysical Research Letters, 1998, 25(21): 4035-4038

[13]

WadgeG, WebleyP W, JamesI N, BingleyR, DodsonA, WaughS, VeneboerT, PuglisiG, MattiaM, BakerD, EdwardsS C, EdwardsS J, ClarkeP J. Atmospheric models, GPS and InSAR measurements of the tropospheric water vapor fields over mount Etna [J]. Geophysical Research Letters, 2002, 29(19): 1905-1908

[14]

WicksC W, ThatcherW, MonasteroF C, HastingM A. Steady state deformation of the Coso Range, east central California, inferred from satellite radar interferometry [J]. Journal of Geophysical Research, 2001, 106B7: 13769-13780

[15]

WrightT J, LuZ, WicksC. Source model for the Mw 6.7, 23 October 2002, Nenana mountain earthquake (Alaska) from InSAR [J]. Geophysical Research Letters, 2003, 30(18): 1974-1977

[16]

BaranI, StewartM P, KampesB M, PerskiZ, LillyP. A modification to the Goldstein radar interferogram filter [J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9): 2114-2118

[17]

LiZ W, DingX L, HuangC, ZhuJ J, ChenY L. Improved filtering parameter determination for the Goldstein radar Interferogram filter [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2008, 63(6): 621-634

[18]

BamlerR, HartlP. Synthetic aperture radar interferometry [J]. Inverse Problem, 1998, 14: R1-R54

[19]

FranceschettiG, LanariRSynthetic aperture radar processing [M], 1999, Florida, USA, CRC Press: 167-222

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