A High-Precision Method for Extracting Surface Deformation Using UAV Images
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摘要: 地表形变监测对于深入探究地质灾害的成因机制、演化特征以及构建综合风险预警系统具有重要意义。针对传统摄影测量技术面临的人工识别地面控制点效率低下和精度不足的问题，提出一种基于无人机影像的高精度地表形变提取方法。首先根据环形编码标志点的图像特征开发了一套精密的自动识别算法，以高效捕捉无人机影像中编码标志点的像平面坐标；然后利用编码标志点在形变前后两期无人机影像中的像平面坐标及地面控制点的物方空间坐标，通过地表形变提取算法快速、准确地解算出地表形变信息；最后设计了无人机航测实验，并将所得结果与全站仪监测结果进行对照分析来验证该方法的可行性。实验结果显示，所提方法可达到亚厘米级精度，精度明显高于点云直接比对算法及多尺度模型到模型点云比对算法，证明该方法能够提取准确的地表形变信息，具有较高的实用价值和良好的应用前景。Abstract: Objectives: Surface deformation monitoring is of great significance for deeply studying disaster formation mechanisms and evolution characteristics, as well as for establishing an integrated risk-based early warning system. Methods: To address the issues of low efficiency and limited accuracy in manually identifying ground control points in traditional photogrammetric methods, a high-precision method for extracting surface deformation using unmanned aerial vehicle (UAV) images was proposed. First, a precise automatic identification algorithm was developed to obtain the image coordinates of coded targets within UAV images based on the characteristics of coded targets. Next, the deformation results were generated quickly and accurately by using the image coordinates of coded targets in the UAV images captured before and after deformation and the object space coordinates of the control points. Results: Total station survey data was used as reference values to compare and verify this method. The UAV aerial survey experiment results validated that this method can achieve sub-centimeter accuracy. Its precision is significantly higher than that of both the direct Cloud-to-Cloud comparison algorithm and the Multiscale Model-to-Model Cloud Comparison algorithm. Conclusions: The proposed method can accurately extract surface deformation data, indicating strong practicality and significant application potential.
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Keywords:
- deformation monitoring /
- UAV images /
- photogrammetry /
- coded targets /
- image processing
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