Work

An Interferometric Phase Optimization Method Joining Polarimetric and Temporal Dimensions

Year: 2025

Type: article

Abstract: The polarimetric phase optimization method has been integrated into the multitemporal synthetic aperture radar interferometry (MT-InSAR) framework to enhance phase quality and deformation coverage, known as multitemporal polarimetric InSAR (MT-PolInSAR) technology. However, most existing MT-PolInSAR methods optimize phase separately in the temporal and polarimetric dimensions, failing to leverage the interdimensional relationships fully. This article proposes a novel multipolarization optimization method, which achieves one-step phase optimization by joining temporal and polarimetric dimensions based on a joint probability density function and maximum likelihood estimation (MLE). Additionally, a no-threshold regularization is employed to strengthen the stability of the multipolarization covariance matrix. The proposed approach has been validated through synthetic and real quad-polarization datasets. Regarding the real data, ALOS-2/PARSAR-2 from the Fengjie landslide in China and Radarsat-2 data from the Barcelona airport in Spain are used. The experimental outcomes demonstrate that our proposed approach significantly diminishes phase noise while increasing the density of measurement points.

Source: IEEE Transactions on Geoscience and Remote Sensing

DOI: 10.1109/tgrs.2025.3556141

Authors: Yaogang Chen, Jun Hu, Jordi J. Mallorqui, Haiqiang Fu, Wenqing Wu, Leixin Zhang

Institutions: Central South University, Universitat Politècnica de Catalunya, China University of Mining and Technology

Language: English

Topic: Synthetic Aperture Radar (SAR) Applications and Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, China Scholarship Council, Ministerio de Ciencia e Innovación

Awards: G1767464481, G2274253727, G7865284385