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Received：25 August 2025，
Published：07 April 2026
移动端阅览
相较于常规时序InSAR即TS-InSAR（Time series Interferometric Synthetic Aperture Radar）技术，相位梯度堆叠方法具有不依赖相位解缠、对大气误差不敏感、计算效率高且受失相干影响小等优点，在广域滑坡识别中具有重要应用潜力。然而，复杂山区地形对相位梯度堆叠结果的影响显著，易产生伪形变信号，制约其应用。针对上述问题，本文系统分析了InSAR几何畸变和DEM误差对相位梯度堆叠结果的影响机理，并提出相应的校正与抑制方法。针对地形起伏引起的几何畸变，提出一种顾及几何畸变的相位梯度堆叠方法，通过引入相邻像元对应的实地距离，统一相位梯度计算的空间基准，有效削弱透视收缩效应。在金沙江上游地区的实验证明，该方法在干涉图数量较少的情况下即可显著降低透视收缩影响，提高复杂山区地表形变的识别能力。实验结果表明，为保证识别结果的完整性，实际应用中相位梯度堆叠的干涉图数量宜不少于180幅。针对DEM误差，本文通过理论推导定量分析了残余地形相位在相位梯度堆叠中的传播特性，结果表明其影响程度与累计垂直基线绝对值成正比。通过优化干涉像对的基线组合、减小累计垂直基线绝对值，可有效抑制DEM误差引起的伪形变信号。理论分析表明，在使用SRTM DEM（标称高程精度约16 m）并对200幅Sentinel-1干涉图进行堆叠的情况下，当累计垂直基线绝对值控制在600 m以内时，DEM误差对相位梯度堆叠结果的影响已与随机噪声水平相当。进一步以汶川地震触发的大光包滑坡为例开展实证分析，验证了所提出方法在复杂山区滑坡识别中的合理性与有效性。
Compared to conventional time-series InSAR (TS-InSAR) techniques
phase gradient stacking offers advantages such as immunity to atmospheric and phase unwrapping errors
fast computational speed
and robustness in low-coherence scenarios
making it well-suited for large-area landslide identification. However
complex mountainous terrain significantly distorts phase gradient stacking results. Firstly
topographic variations cause severe geometric distortions in SAR images
leading to interferometric phase artifacts. Secondly
the external Digital Elevation Model (DEM) used in differential interferometry inevitably contains errors
and the residual topographic phase in interferograms introduces false deformation signals in the phase gradient results
hindering accurate landslide detection. This study provides an in-depth analysis of topographic effects in phase gradient stacking and proposes corresponding correction methods. A divide-and-conquer approach is adopted to address the two types of terrain-induced errors. To mitigate geometric distortions
the actual ground distance between adjacent pixels is calculated and used to correct phase-gradient estimates
thereby unifying the spatial reference for gradient computation across the entire interferogram and reducing foreshortening effects. For residual topographic phases
a strict theoretical derivation based on their generation mechanism and the stacking algorithm demonstrates that their magnitude is proportional to the absolute value of the cumulative perpendicular baseline. Based on this relationship
a reference threshold for the cumulative baseline is derived
and a greedy algorithm is designed to efficiently select an optimal baseline subset that meets the threshold requirement and minimizes DEM-induced errors. Experimental results in the Guxue section of the Jinsha River demonstrate that the improved phase gradient stacking method effectively eliminates foreshortening effects with only 120 interferograms and significantly enhances deformation detection capability. In contrast
conventional methods still exhibit distortions even with over 200 interferograms
confirming the effectiveness of the proposed geometric distortion correction. Further validation using the Daguangbao landslide triggered by the Wenchuan earthquake shows a clear positive correlation between the residual topographic phase error and the absolute cumulative vertical baseline but largely independent of the number of interferograms. Reducing the absolute cumulative vertical baseline effectively suppresses the impact of DEM errors. Correcting the phase gradient using the actual distance between adjacent pixels unifies the spatial reference for gradient calculation across the interferogram
effectively alleviating foreshortening and improving deformation identification capability in complex mountainous areas. The experimental results also indicate that
in practical applications
more than 180 interferograms should be used for stacking to ensure the completeness of the detection results. The residual topographic phase error is proportional to the absolute cumulative vertical baseline. By optimizing baseline selection to control the cumulative vertical baseline
this error can be significantly reduced
enhancing the accuracy and stability of phase gradient stacking results.
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DO 1 : 10.11947/j.AGCS.2022.20220294
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