An adaptive patch-based goldstein filter for interferometric phase denoising

The Goldstein filter was a well-known approach of synthetic aperture radar interferogram filtering for its simplicity and high efficiency. The filtering performance of Goldstein and its improved filters is mostly affected by several parameters, such as the noise suppression parameter alpha, the sliding patch size, and the kernel window size. Yet, the fixed parameters will cause the filtering intensity to mismatch the noise levels of different regions. In this paper, we propose an adaptive patch-based Goldstein filter (AP-Goldstein filter), which takes the pseudo-variation coefficient as the measure of patch homogeneity and adaptively selects the patch sizes in different regions of interferogram. This filter controls the noise suppression parameter alpha by pseudo-coherence, which is a common and effective indicator applied to filter the interferogram phase. Meanwhile, the previously determined patch size of each pixel is used to confirm the kernel window size at the same region. The proposed and other advanced filters were tested on real and simulated interferograms. The qualitative and quantitative comparison of the experimental results shows that this new method can better restrain the phase noise of interferogram while maintaining the edge detailed information and can improve the automation of filtering.

Automated summary

In this paper, an adaptive patch-based Goldstein filter (AP-Goldstein filter) is proposed, which adapts patch sizes based on pseudo-variation coefficient and controls noise suppression parameter alpha through pseudo-coherence. The proposed method effectively suppresses phase noise of interferograms while maintaining edge detailed information and improving filtering automation.