Urban Damage-Level Estimation With Reconstructed Quad-Pol SAR Data From Dual-Pol SAR Mode

Urban damage investigation is an important application for polarimetric synthetic aperture radar (SAR), which is capable of sensing the target scattering mechanism changes before and after a natural disaster. Quad-pol SAR with fully polarimetric acquisition capability can better sense the scattering mechanism changes. Meanwhile, dual-pol SAR with wider swath is suitable for large area monitoring. In this vein, this work dedicates to generating pseudo quad-pol SAR data from dual-pol SAR mode to partially reconstruct fully polarimetric information. The main contributions contain two aspects. Firstly, a multi-scale feature aggregation convolutional neural network (CNN) has been proposed to reconstruct quad-pol SAR data, which includes a feature extraction (FE) module to collect multi-scale features from dual-pol SAR data in spatial and polarimetric domain, and a feature translation (FT) network aggregated with attention modules to deeply fuse the stacked multi-scale features and map them to quad-pol SAR covariance matrices. Then, a urban damage level estimation approach has been established with reconstructed quad-pol SAR data based on polarimetric coherence pattern interpretation tool. Experimental studies have been carried out in terms of both pseudo quad-pol SAR data reconstruction and urban damage level estimation. Comparison results demonstrate that the proposed method achieves better quad-pol SAR data reconstruction accuracy and urban damage level estimation accuracy. Moreover, compared with the urban damage level estimated by real quad-pol SAR data, the proposed method can achieve 99.83% estimation consistency within 5% error tolerance.