在库坝区域边坡的连续变形监测应用中,地基合成孔径雷达干涉测量(GB-InSAR)容易受到测区大气环境变化干扰,导致其干涉图序列变形解算结果不够精确;同时大数量的连续GB-SAR影像处理过程较为耗时,影响了GB-InSAR整体解算效率和准实时的变形分析应用需求。针对上述问题,在常规多项式大气改正方法的基础上,引入了基于相位梯度的均匀格网采样法和干涉图叠加法,构建了一种基于抽稀高质量像元(HQP)的多项式大气改正方法,并将该方法应用于黄登水电站施工期右岸高边坡变形监测中。分析结果表明,二元多项式大气模型的RMSE均值为0.039 5 rad,明显优于一元模型和其他常规大气改正方法。该抽稀HQP方法RMSE均值为0.024 0 rad,同抽稀前精度相当,但整体解算时间由2.32 h大幅缩减至0.80 h,说明该方法能够在保证建模精度的基础上显著提高GB-SAR连续影像大气改正处理效率,可为边坡安全监测提供有效技术支持。

Continuous slope monitoring in reservoirs and dams using ground-based synthetic aperture radar interferometry (GB-InSAR) is vulnerable to atmospheric environmental fluctuations. These fluctuations can cause inaccuracies in deformation results derived from interferogram sequences. Moreover, processing large volumes of continuous GB-SAR images is time-consuming, which negatively affects the overall efficiency of GB-InSAR and the feasibility of quasi-real-time deformation analysis applications. To tackle these problems, this paper presents a uniform grid sampling method and interferometric stacking technique based on the phase gradient building on the conventional polynomial atmospheric correction method. A polynomial atmospheric correction method based on downsampled high-quality pixels (HQPs) is then constructed. This method is applied to monitor the deformation of the high slope on the right bank during the construction of the Huangdeng Hydropower Station. Experimental results show that the root mean square error (RMSE) of the binary polynomial model averages 0.039 5 rad, significantly outperforming that of the unitary model and other conventional correction methods. The average RMSE of the proposed method is 0.024 0 rad, comparable to the accuracy before downsampling. However, the overall solution time reduces notably from 2.32 h to 0.80 h. This indicates that the proposed method can significantly improve the efficiency of continuous image atmospheric correction while maintaining modeling accuracy, offering effective technical support for slope safety monitoring.