INSAR Interferogram Filtering Based on Bayesian Threshold in Stationary Wavelet Domain

Wang Pei; Wang Yan-fei; Zhang Bing-chen; Tang Yu; Ma Li-xiang

doi:10.3724/SP.J.1146.2006.00428

Volume 29 Issue 11

Jan. 2011

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(11): 2706-2710

Wang Pei, Wang Yan-fei, Zhang Bing-chen, Tang Yu, Ma Li-xiang. INSAR Interferogram Filtering Based on Bayesian Threshold in Stationary Wavelet Domain[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2706-2710. doi: 10.3724/SP.J.1146.2006.00428

Citation:

Wang Pei, Wang Yan-fei, Zhang Bing-chen, Tang Yu, Ma Li-xiang. INSAR Interferogram Filtering Based on Bayesian Threshold in Stationary Wavelet Domain[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2706-2710. doi: 10.3724/SP.J.1146.2006.00428

Citation:

PDF( 605 KB)

INSAR Interferogram Filtering Based on Bayesian Threshold in Stationary Wavelet Domain

doi: 10.3724/SP.J.1146.2006.00428

Received Date: 2006-04-06
Rev Recd Date: 2006-09-21
Publish Date: 2007-11-19

Abstract

Abstract

Noise in the interferogram hinders the processing of two-dimensional phase unwrapping, and decreases the accuracy of the final DEM products. In this paper a interferometric phase noise reduction algorithm, in the stationary wavelet domain, is proposed. The algorithm chooses threshold of wavelet coefficients adaptively by using Bayesian method, and adaptively selects the best scale of two dimensional stationary wavelet transform for filtering. By using both simulated and SIR-C/X SAR generated interferograms, the performance of the algorithm is demonstrated and compared with the mean filter, the median filter and the Goldstein filter. By processing the simulated data, it is proved that the algorithm can get a result with better RMS and coherence. By using the algorithm, the residue number of real data reduced from 30430 to 113, far below the other methods. The result shows that the algorithm can preserve the fringes better, and filter the phase noise more effectively by reducing the number of residues. And the algorithm has some advantages over the Goldstein filter.

FullText(HTML)

References(1)

References

Hellwich O. Basic principles and current issues of SAR interferometry.www.ipi.uni-.hannover.de/html/publikationen/1999/isprs-workshop/cd/pdf-papers/hellwich.pdf.[2]Burgmann R, Rosen P A, and Fielding E J. Synthetic aperture radar interferometry to measure earth surface topography and its deformation[J].Annual. Review of Earth and Planetary Sciences.2000, 28:169-209[3]Lee J S, Papathanassiou K P, and Ainsworth T L. A new technique for noise filtering of SAR interferometric phase images[J].IEEE Trans. on Geoscience and Remote Sensing.1998, 36(5):1456-1465[4]Goldstein R M and Werner C L. Radar interferogram filtering for geophysical applications[J].Geophysical Research Letters.1998, 25(21):4035-4038[5]Martnez C L and Fbregas X. Modeling and reduction of SAR interferometric phase noise in the wavelet domain[J].IEEE Trans. on Geoscience and Remote Sensing.2002, 40(12):2553-2566[6]MALLAT S. 杨力华, 戴道清, 黄文良译. 信号处理的小波导引[M]. 北京: 机械工业出版社, 2002: 115-117.[7]Lee J S, Hoppel K W, and Mango S A. Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery[J].IEEE Trans. on Geoscience and Remote Sensing.1994, 32(5):1017-1027[8]Lopez C and Fabregas X. SAR interferometric phase statistics in wavelet domain[J].Eelctronics Letters.2002, 38(20):1207-1208[9]Martinez C and Canovas X. Results on SAR interferometric phase noise reduction using wavelet transform, 4th European Conference on Synthetic Aperture Radar, Cologne, Germany, 4-6 June 2002, P7: 593-596.[10]Carlos Lpez-Martnez and Xavier Fbregas. SAR interferometric phase denoising. A new approach based on wavelet transform. Proceedings of SPIE, 2000, 4173: 199-210.[11]Simoncelli E and Adelson E. Noise removal via Bayesian wavelet coring. Proc. IEEE Int. Conf. Image Processing, Lausanne, Switzerland, Sept. 1996, 1: 379-382.[12]Chang S C, Yu B, and Vetterli M. Adaptive wavelet thresholding for image denoising and compression[J].IEEE Trans.on Image Processing.2000, 9(9):1532-1546[13]Donoho D L. De-noising by soft-thresholding[J].IEEE Trans. on Information Theory.1995, 41(4):613-627

Relative Articles

Supplements(0)

Cited By

Proportional views