无需精确配准，直接从SAR图像预测相干性。
Beyond Backscatter: InSAR coherence from detected SAR images
- 用残差U-Net学习后向散射与相干性的关系。
- 在多数据集上优于传统强度方法，精度更高。
- 可部署于全球分析就绪数据，适合大范围应用。
本文提出一种深度学习框架，直接从检测后的合成孔径雷达（SAR）图像中回归相干性，无需精确配准。采用残差U-Net模型，利用高精度配准的哨兵1号SLC数据对生成的相干图进行训练，学习后向散射幅值与相干性的映射关系。模型在12天间隔的SLC数据对上训练，并在多种数据集上评估，涵盖配准的SLC产品和公开的分析就绪数据，覆盖不同辐射特性、几何形态及地理区域。实验表明，该方法实现高分辨率相干性回归，精度显著优于现有基于强度的方法。网络在不同地理区域及未参与训练的时间基线间具有良好泛化能力。此外，其可在全球可获取的分析就绪数据（如通过谷歌地球引擎分发的地距检测数据）上运行，适用于任务设计、变化监测和多种制图任务的大规模应用。
原文摘要 · Abstract (English)
In this work, we propose a deep learning framework for coherence regression directly from detected SAR images, without the need for accurate coregistration. A Residual U-Net is trained using coherence maps derived from precisely coregistered Sentinel-1 SLC data to learn the relationship between backscatter magnitudes and coherence. The model is trained on 12-day SLC pairs and evaluated across different datasets, including coregistered SLC products and open access analysis-ready data, covering diverse radiometric properties, geometries, and locations. Experimental results demonstrate that the proposed method achieves high-resolution coherence regression with improved accuracy compared to existing intensity-based approaches. The network generalizes well across diverse geographical locations and even across different temporal baselines that were never seen at training time. Additionally, the ability to operate on globally available analysis-ready data, such as ground range detected data, e.g., distributed through Google Earth Engine, enables its large-scale application in mission design, change monitoring, and diverse mapping tasks.
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