Shape-to-Scale InSAR Adaptive Filtering and Phase Linking under Complex Elliptical Models

2025-09-16

AI Summary

In SAR interferometry (InSAR), deformation inversion using distributed scatterers (DS) is limited by two key assumptions: conventional amplitude-similarity-based pixel selection struggles to distinguish scatterers with similar amplitudes but differing coherence, while the standard complex circular Gaussian phase model lacks robustness under non-Rayleigh amplitude fluctuations. To address these limitations, this paper proposes a “Shape-to-Scale” framework. First, it introduces a complex angular central Gaussian (C-ACG) model to jointly characterize amplitude and phase distributions, enabling scatterer structure-consistency-driven pixel selection. Second, it develops a robust phase estimator based on the complex generalized Gaussian (CGG) distribution and incorporates scale-invariant second-order statistics for adaptive angular consistency filtering and phase linking. Experimental results demonstrate substantial improvements in coherent structure clustering accuracy and phase estimation stability, particularly for high-resolution SAR time-series deformation analysis.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Distributed scatterers in InSAR (DS-InSAR) processing are essential for retrieving surface deformation in areas lacking strong point targets. Conventional workflows typically involve selecting statistically homogeneous pixels based on amplitude similarity, followed by phase estimation under the complex circular Gaussian model. However, amplitude statistics primarily reflect the backscattering strength of surface targets and may not sufficiently capture differences in decorrelation behavior. For example, when distinct scatterers exhibit similar backscatter strength but differ in coherence, amplitude-based selection methods may fail to differentiate them. Moreover, CCG-based phase estimators may lack robustness and suffer performance degradation under non-Rayleigh amplitude fluctuations. Centered around scale-invariant second-order statistics, we propose ``Shape-to-Scale,'' a novel DS-InSAR framework. We first identify pixels that share a common angular scattering structure (``shape statistically homogeneous pixels'') with an angular consistency adaptive filter: a parametric selection method based on the complex angular central Gaussian distribution. Then, we introduce a complex generalized Gaussian-based phase estimation approach that is robust to potential non-Rayleigh scattering. Experiments on both simulated and SAR datasets show that the proposed framework improves coherence structure clustering and enhances phase estimation robustness. This work provides a unified and physically interpretable strategy for DS-InSAR processing and offers new insights for high-resolution SAR time series analysis.

Problem

• Improving distributed scatterer identification using shape statistics
• Enhancing phase estimation robustness under non-Rayleigh scattering
• Developing adaptive filtering for complex elliptical InSAR models

Innovation

• Shape-to-Scale framework using angular statistics
• Angular consistency adaptive filter for pixel selection
• Complex generalized Gaussian-based robust phase estimation