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IF Estimation in the Interferograms Using Wavelet Transformation

Yanjie Zhang(1) and Prinet Veronique(1)

(1) National Laboratory of Pattern Recognition, the Institute of Automation, 100080, China

Abstract

Because the space-borne SAR can acquire high resolution images all the time with little affect to the climate and cloud, interferometric SAR (InSAR) has been widely applied to create the digital elevation model (DEM); specially, using the differential interferometric SAR (DInSAR), people can detect subtle change of the surface with a precision of cm- mm. Phase unwrapping is a key point in Interferometric SAR (InSAR) processing. Usually phase unwrapping is composed of two steps: (1) phase gradient estimation of the interferogram, (2) the integration of the estimated phase gradients to obtain an unwrapped phase surface [1]. Although there already have been many different phase unwrapping methods, they differ in the two steps. Because the coherence of the SAR images is affected by many factors such as thermal noise, atmospheric effect, the imaging geometry, the different Doppler Centroid, the processing and so on, all of them add the difficulty to phase- unwrapping. Before doing phase unwrapping, the interferograms must be filtered. It has been proved that the local frequency of the interferogram is related to the local terrain slope of the scene, so it can be used to smooth the interferotgram and reduce the phase noise by slope-adaptive filtering. In [4], the authors provide a building extraction method by filtering layover areas where the spectral shift can be partially used to separate layover and non-layover areas in high- resolution IFSAR. We have developed a novel interferogram filtering method. After filtering, the number of the residual points in the interferograms is greatly reduced and the fringes are clear. The estimated local frequency makes phase unwrapping feasible even for very noisy interferograms [3]. In our work we use the wavelet transformation to estimate the local frequency. For a real signal we can get the IF by doing Hilbert Transformation. In [2], the authors showed the estimation method using Gabor Tansformation. Because the size of the window of the Gabor Transformation is fixed, it can’t reflect the fast changing local frequency, while by using the Wavelet Transformation, it can be changed by different scale that is called multi-resolution analysis. The ridge of Wavelet Transformation can be used to extract the instantaneous characteristic precisely.

References

1. R. Klees, “SAR Interferometry Technology”, IAG Special Study Group 2.160 Period 1995-1999. 2. P.V., Diego, et al, “Local Frequency Estimation in Interferograms Using a Multiband Pre-Filtering Approach”. 3. E. Trouve, et al, “Improving phase unwrapping techniques by the use of local frequency estimates”, IEEE Transactions on Geosciences and Remote Sensing, Vol. 36, Nov. 1998. 4. D. Petit, et al, “The filtering of layover areas in high- resolution IFSAR for the building extraction”, SAR Image Analysis, Modeling, and Techniques III, Proceedings of SPIE Vol. 4173, 2000.

 

Full paper

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry