Urban damage investigation is an important application for a polarimetric synthetic aperture radar (SAR), which is capable of sensing the target scattering mechanism changes before and after a natural disaster. A quad-pol SAR with fully polarimetric acquisition capability can better sense the scattering mechanism changes. Meanwhile, a 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. First, a multiscale feature aggregation convolutional neural network (CNN) has been proposed to reconstruct quad-pol SAR data, which includes a feature extraction (FE) module to collect multiscale 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 multiscale features and map them to quad-pol SAR covariance matrices. Then, an 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 (EC) within 5% error tolerance (ET).

中文翻译：

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利用双极化 SAR 模式重建的四极化 SAR 数据估计城市损坏程度

城市损害调查是极化合成孔径雷达（SAR）的重要应用，它能够感知自然灾害前后目标散射机制的变化。具有全偏振采集能力的四极化SAR可以更好地感知散射机制的变化。同时，测绘带较宽的双极化SAR适合大面积监测。本着这种精神，这项工作致力于从双极化 SAR 模式生成伪四极化 SAR 数据，以部分重建完全极化信息。主要贡献包含两个方面。首先，提出了一种多尺度特征聚合卷积神经网络（CNN）来重建四极化SAR数据，其中包括一个特征提取（FE）模块，用于从空间域和极化域的双极化SAR数据中收集多尺度特征，以及一个特征提取模块。翻译（FT）网络与注意力模块聚合，以深度融合堆叠的多尺度特征并将其映射到四极点 SAR 协方差矩阵。然后，基于极化相干模式解释工具，利用重建的四极化SAR数据建立了城市受损程度估计方法。在伪四极化SAR数据重建和城市受损程度估计方面开展了实验研究。对比结果表明，该方法具有较好的四极化SAR数据重建精度和城市受损程度估计精度。此外，与真实四极化SAR数据估计的城市损害水平相比，该方法在5%的误差容限（ET）内可以实现99.83%的估计一致性（EC）。