Improved Goldstein Interferogram Filter Based on Local Fringe Frequency Estimation

Abstract

The quality of an interferogram, which is limited by various phase noise, will greatly affect the further processes of InSAR, such as phase unwrapping. Interferometric SAR (InSAR) geophysical measurements’, such as height or displacement, phase filtering is therefore an essential step. In this work, an improved Goldstein interferogram filter is proposed to suppress the phase noise while preserving the fringe edges. First, the proposed adaptive filter step, performed before frequency estimation, is employed to improve the estimation accuracy. Subsequently, to preserve the fringe characteristics, the estimated fringe frequency in each fixed filtering patch is removed from the original noisy phase. Then, the residual phase is smoothed based on the modified Goldstein filter with its parameter alpha dependent on both the coherence map and the residual phase frequency. Finally, the filtered residual phase and the removed fringe frequency are combined to generate the filtered interferogram, with the loss of signal minimized while reducing the noise level. The effectiveness of the proposed method is verified by experimental results based on both simulated and real data.

An Interferometric Phase Noise Reduction Method Based on Modified Denoising Convolutional Neural Network

“…The simulation parameters are shown in Table 1. The slope adaptive filter [27] and improved Goldstein filter [28] are used for comparison.…”

“…From the phase error diagrams, it is clear that the IPDnCNN method performs better than the other filters. In order to quantitatively evaluate the results, mean square error (MSE), edge preservation index (EPI) and residues are used as the criteria [28]. MSE is to measure the deviation of the denoised phase from the clean one, given by…”

“…In [27], multi-frequency data is used to achieve an accurate LFF in abruptly changing terrain and the Goldstein filter is applied to the residual phase. In [28], the local fringe frequency is removed before Goldstein filtering and the filter parameters are then optimized, which improves edge preservation. Nevertheless, the performance of local frequency compensation filters relies on frequency estimation accuracy, which is heavily influenced by phase noise and window size.…”

Smart filtering of phase residues in noisy wrapped holograms

“…In contrast, numerical techniques for phase unwrapping have become more popular with the improvement in computer processing speed and the reduction in computational costs. Numerical techniques are mainly based on either directly unwrapping [21][22][23][24][25][26][27] or filtering [28][29][30][31][32][33][34][35] the wrapped phase. Examples of the first of these types of numerical techniques have been developed using global algorithms 23,24 , path-following algorithms 25 , and region-based algorithms 26,27 .…”

Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry

“…First, the local fringe frequency estimation is realized by the maximum likelihood (ML) method [ 32 , 33 ]. Subsequently, to preserve the fringes of real targets and strengthen the feature of the false part, the estimated fringe frequency in each filtering window is removed from the original noisy phase.…”