An Interferometric Phase Optimization Method Jointing Polarimetric and Temporal Dimensions

The polarimetric phase optimization has been effectively incorporated into the multi-temporal synthetic aperture radar interferometry (InSAR, MT-InSAR) to improve phase estimation quality and extend deformation monitoring coverage. This technique, commonly called multi-temporal polarimetric InSAR (MT-PolInSAR), has shown great potential in enhancing interferometric measurements for various geophysical applications, including deformation monitoring and disaster assessment. However, most existing MT-PolInSAR methods optimize phase independently along the temporal and polarimetric dimensions, which neglects the potential synergies between these two aspects. As a result, the capability of polarimetric and temporal information for phase optimization is not utilized fully, leading to suboptimal results, which reduces the effectiveness of deformation analysis in complex scenarios, such as landslides, subsidence, and fault movement. To address these limitations, this study proposes a novel multi-polarization optimization method that achieves one-step phase optimization by jointly considering the temporal and polarimetric dimensions. The proposed method is based on a joint probability density function of the multi-polarization covariance matrix and maximum likelihood estimation method, which enable a more comprehensive optimization of phase information by leveraging the inherent relationships between the temporal and polarimetric dimensions. Unlike traditional methods that treat these dimensions independently, the proposed approach effectively combines the strengths of both dimensions to achieve superior phase quality. Additionally, a no-threshold regularization technique is employed in this method to enhance the stability of the multi-polarization covariance matrix. This regularization eliminates the need for manual thresholding based on an analytical solution, avoiding relying on empirical threshold values. This approach significantly enhances the reliability and consistency of the optimization process, especially in scenarios with high noise levels or challenging scattering conditions. The effectiveness of the proposed approach has been validated using both synthetic and real quad-polarization datasets. Synthetic data experiments were conducted to evaluate the method’s ability to handle varying noise levels and scattering mechanisms. For real data validation, two datasets were utilized: ALOS-2/PALSAR-2 data from the Fengjie landslide region in China and Radarsat-2 data from the Barcelona airport in Spain. These datasets cover diverse scenarios with different levels of complexity and provide an excellent testbed for assessing the performance of the proposed method. The experimental results demonstrate that the proposed approach significantly reduces phase noise compared to traditional MT-PolInSAR methods, leading to a more accurate representation of deformation signals. Furthermore, the method achieves a notable increase in the density of measurement points, which is crucial for applications requiring high spatial resolution and coverage. In the case of the Barcelona airport, the proposed approach successfully identified subtle deformation patterns that were otherwise obscured by noise in traditional methods. Similarly, in the Fengjie landslide dataset, the method provided a clearer and more detailed phase distribution, which could enhance the monitoring of landslide.