Atmospheric effect represents one of the major error sources for interferometric synthetic aperture radar (InSAR), particularly for the repeat-pass InSAR data. In order to further improve the practicability of InSAR technology, it is essential to study how to estimate and eliminate the undesired impact of atmospheric effects. In this paper, we propose the multi-resolution weighted correlation analysis (MRWCA) method between the dual-polarization InSAR data to estimate and correct atmospheric

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... ects for InSAR topographic mapping. The study is based on the a priori knowledge that atmospheric effects is independent of the polarization. To find the identical atmospheric phase (ATP) signals of interferograms in different polarizations, we need to remove the other same or similar phase components. Using two different topographic data, differential interferometry was firstly performed so that the obtained differential interferograms (D-Infs) have different topographic error phases. A polynomial fitting method is then used to remove the orbit error phases. Thus, the ATP signals are the only identical components in the final obtained D-Infs. By using a forward wavelet transform, we break down the obtained D-Infs into building blocks based on their frequency properties. We then applied weighted correlation analysis to estimate the wavelet coefficients attributed to the atmospheric effects. Thus, the ATP signals can be obtained by the refined wavelet coefficients during inverse wavelet transform (IWT). Lastly, we tested the proposed method by the L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR dual-polarization SAR data pairs covering the San Francisco (USA) and Moron (Mongolia) regions. By using Ice, Cloud, and land Elevation Satellite (ICESat) data as the reference data, we evaluated the vertical accuracy of the InSAR digital elevation models (DEMs) with and without atmospheric effects correction, which shows that, for the San Francisco test site, the corrected interferogram could provide a DEM with a root-mean-square error (RMSE) of 7.79 m, which is an improvement of 40.5% with respect to the DEM without atmospheric effects correction. For the Moron test site, the corrected interferogram could provide a DEM with an RMSE of 10.74 m, which is an improvement of 30.2% with respect to the DEM without atmospheric effects correction.