As a typical application of remote sensing technology, change detection can find the ground information changes by acquiring the images of the same region at different times. The change detection using the synthetic aperture radar (SAR) with the advantages of all day and all-weather usually monitors the significant surface change, such as flood disasters and earthquake deformation. However, when it comes to detecting subtle changes such as vehicle traces, the traditional methods ignoring the phase coherence between image pairs cannot intensify these faint changes in the difference image. The SAR coherent change detection (CCD) based on repeat-pass repeat-geometry complex images utilizing both the intensity and phase fraction could exhibit the subtle vehicle trace in the difference image. However, the complicated background and decorrelation factors significantly affect the quality of difference images, further causing great trouble for automatic trace detection. This article proposes the spatial feature enhanced Unet and adaptive data augmentation to realize vehicle trace detection. More specifically, the pseudocolor image is first synthesized based on a two-stage coherence estimation method. Then, considering the long-continuity and parallel distribution of vehicle trace samples, the enhanced Unet is constructed by fusing spatial convolutional neural network and spatial attention mechanism. After that, the adaptation data augmentation strategy is presented by introducing manual registration errors and multiple estimation windows. Finally, the experimental results on the Sandia CCD data and our measured data demonstrate the effectiveness of the proposed method.

中文翻译：

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具有空间特征增强的 Unet 和自适应增强的两通 SAR 相干变化检测图像中的车辆轨迹检测

作为遥感技术的典型应用，变化检测可以通过获取同一区域不同时间的图像来发现地面信息的变化。利用合成孔径雷达（SAR）的变化检测具有全天候、全天候的优势，通常监测重大的地表变化，如洪水灾害和地震变形。然而，在检测车辆轨迹等细微变化时，忽略图像对之间相位相干性的传统方法无法强化差异图像中的这些微弱变化。基于重复通过重复几何复杂图像的SAR相干变化检测（CCD）利用强度和相位分数可以在差分图像中表现出细微的车辆轨迹。然而，复杂的背景和去相关因素显着影响了差分图像的质量，进一步给自动轨迹检测带来了很大的麻烦。本文提出空间特征增强Unet和自适应数据增强实现车辆轨迹检测。更具体地，首先基于两阶段相干估计方法合成伪彩色图像。然后，考虑车辆轨迹样本的长连续性和平行分布，融合空间卷积神经网络和空间注意力机制构建增强的Unet。之后，通过引入手动配准误差和多个估计窗口，提出了自适应数据增强策略。最后，