Adaptive sequential estimator for InSAR time series phase estimation

The coherence estimation errors in phase linking can be mitigated through the weighted alignment of interferometric pairs and the intermediate filtering of data subsets. The Sequential Estimator (SE) serves as a representative method. It divides the coherence-weighted matrix into smaller subsets, using image compression and recursive estimation to enhance phase linking. However, the SE method has inherent limitations due to its dependence on fixed subset size and manual parameter setting, which hinder its application in complex, natural scenarios. In such environments, the distributions of coherent and low-coherence signals are often unpredictable. To address such limitations, this paper proposes an Adaptive Sequential Estimator (ASE) method. First, an adaptive coherence-weighted matrix partitioning method is proposed. Utilizing Otsu’s algorithm and a local subset merging algorithm, it adaptively generates data subsets which are dynamically tailored to the coherence distribution. Second, a modified sequential estimator is proposed. It selects the optimal subsets from the list with multiple merging degrees, to guide image compression and recursive phase estimation. Based on these, the ASE method adaptively prioritizes coherent information while minimizing the impact of decorrelation noise, thereby improving phase estimation accuracy. Experimental evaluation is conducted using 30 Radarsat-2 SAR images with VV polarization, including the quantitative and visual comparisons between the ASE method and existing methods. The results indicate that the ASE method outperforms other methods, and is particularly well-suited to handling the variable coherence matrix in natural scenarios. Compared to SE, the ASE method increases the distributed scatterer point density with 7%.