Improved Goldstein SAR Interferogram Filter Based on Empirical Mode Decomposition

The Goldstein filter is one of the most commonly used synthetic aperture radar (SAR) interferogram filters. This letter proposes a new method to find filter parameters of the Goldestein filter based on noise level derived by the empirical mode decomposition (EMD) method. The filtering parameter determined by this method has a definite physical meaning. We used bidimensional empirical mode decomposition (BEMD) to extract features of an interferometric phase image into multiple scales of spatial frequencies, called intrinsic mode functions (IMF). We constructed a pseudo-SNR (signal-to-noise ratio) with the given IMF component, then the new parameter was applied to the Goldstein filtering method in place of the original fixed value ascertained artificially. The results from simulation and real data show that the performance of the new algorithm outperforms the original Goldstein filter, and its enhanced version, the Baran filter. The quantitative evaluation also shows that modification based on the EMD proposed in our paper minimizes the loss of phase while still reducing the level of noise in an interferogram.