Interferometric Phase Optimization Based on PolInSAR Total Power Coherency Matrix Construction and Joint Polarization-Space Nonlocal Estimation

Interferometric phase optimization is important key processing for ensuring the application performance of interferometric synthetic aperture radar (InSAR) technology. The noise's standard deviation depends on the number of looks and the coherence magnitude. Usually, the coherence estimation uses statistical averaging with spatial samples to reduce the speckle noise in interferometric phase images. It has been demonstrated that polarization plays a significant role in the variation of interferometric complex coherence. Currently, InSAR technology utilizes polarimetric information to develop the coherence optimization theory for improving the phase quality. However, the observed coherence region in the complex unitary circle is usually biased from the free-noise one due to the finite multilooking effect and the practical scene heterogeneity, which makes the coherence optimization unstable. In contrast, based on the coherence estimation theory, this article proposes taking polarimetric information as the statistical samples for constructing polarimetric InSAR (PolInSAR) total power (TP) coherency matrix and performs a joint polarization-space nonlocal estimation. Simulated and real experimental results demonstrate that the proposed method improves the performance of the interferometric phase optimization in these three aspects compared with traditional coherence optimization, including phase quality improvement, the number of high coherent points, and computational efficiency.

Automated summary

Interferometric phase optimization is crucial for ensuring the application performance of InSAR technology. This article proposes a method that utilizes polarimetric information to construct a coherence matrix and performs joint estimation, addressing the issues of coherence deviation caused by finite multilooking effect and scene heterogeneity. The proposed method improves phase quality, increases the number of high coherent points, and enhances computational efficiency compared to traditional coherence optimization.