Quad-pol reconstruction of dual-pol SAR data via a physically constrained diffusion model for building damage assessment

• A positive semi-definite constraint module enhances quad-pol data reconstruction. • A Plug-and-play parameter fine-tuning module improves data reconstruction quality. • A DoP frequency feature analysis method enables precise building damage assessment. Quad-polarimetric (quad-pol) synthetic aperture radar (SAR) data provides crucial polarimetric information for post-disaster building damage assessment. However, most current spaceborne SAR platforms prioritize dual-polarization (dual-pol) mode, which ensures high temporal and spatial data availability but limits damage analysis accuracy due to the absence of some polarimetric information. Existing methods for reconstructing dual-pol to quad-pol SAR data often fail to ensure that the reconstructed data meets fundamental physical properties, while traditional building damage detection methods still struggle to accurately capture complex depolarization effects. To address these challenges, this paper proposes a diffusion model-based method for reconstructing dual-pol data to quad-pol data, applied to post-earthquake building damage analysis. The method introduces a Positive Semi-definite Constraint Module and a Plug-and-Play SVD Parameter Fine-tuning Module to ensure the physical validity and accuracy of the reconstructed data. Additionally, a Stokes vector-based Degree of Polarization frequency analysis method is proposed to enhance the description of depolarization information. A multi-dimensional polarimetric feature combination is constructed for grid-level building damage assessment. Experiments on Gaofen-3, ALOS-2/PALSAR-2, and Sentinel-1 data show that the proposed method performs optimally in complex scenarios, with all pixels meeting the positive semi-definite constraint. Compared to the original dual-pol SAR data, building damage assessment using the reconstructed quad-pol SAR data resulted in an F1 score improvement of 16.3% and 8.4% for detecting moderately and severely damaged buildings, respectively. This research provides crucial technical support for fully harnessing the potential of dual-pol SAR data in building damage assessment.