| 利用距离变换模型进行卫星影像与激光点云精配准 |
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| 引用本文： | 张永军,洪玮辰,万一. 利用距离变换模型进行卫星影像与激光点云精配准[J]. 武汉大学学报(信息科学版), 2023, 48(3): 339-348. DOI: 10.13203/j.whugis20220028 |
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| 作者姓名： | 张永军 洪玮辰 万一 |
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| 作者单位： | 1.武汉大学遥感信息工程学院, 湖北 武汉, 430079 |
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| 基金项目： | 国家自然科学基金42030102国家自然科学基金42192583国家自然科学基金42001406中国博士后基金2020M672416 |
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| 摘 要： | 卫星影像可以低成本、高频率地提供地物光谱特性观测信息，而激光点云可以提供精细的几何结构，两类数据的融合可以实现优势互补，进一步提高地物分类和信息提取的精度和自动化程度。实现亚像素级精度的几何配准是实现两类数据融合的前提，提出了一种基于线元素距离变换模型的快速配准方法。该方法以点云为控制源，将点云中的建筑物边缘等典型线元素通过卫星影像的初始有理多项式系数(rational polynomial coefficient, RPC)投影到像方空间，与卫星影像中的线元素进行迭代最近点配准，从而通过RPC参数精校正的方式实现几何配准。采用距离变换模型作为迭代最近点搜索的查找表，提高了运算效率；采用最新的渐进式鲁棒求解策略，能在噪声极多的情况下保证配准的鲁棒性。采用GeoEye-2、高分七号、WorldView-3等卫星影像与激光点云进行了配准实验，并分别通过人工精确量测的外业控制点和作业员内业刺的控制点作为检查，证明所提方法能在3种影像上达到0.4～0.7 m的配准精度，显著优于将点云映射为二维图像然后通过多模态匹配进行配准的策略。
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| 关 键 词： | 卫星影像 激光点云 几何配准 距离变换模型 鲁棒求解 |
| 收稿时间： | 2021-12-20 |
Registration of HRSI and LiDAR Point Clouds Based on Distance Transformation Model |
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| Affiliation: | 1.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China |
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| Abstract: | Objectives High resolution satellite images (HRSI) can provide spectral characteristics observation information of ground objects at low cost and high frequency, while light detection and ranging(LiDAR) point clouds can provide fine geometric structure. The fusion of two kinds of data can realize complementary advantages, and further improve the accuracy and automation of ground object classification and information extraction. The realization of geometric registration with sub-pixel accuracy is the premise of two kinds of data fusion. Methods A fast registration method based on line element distance transformation model is proposed. The point clouds are used as the control source, and the typical line elements such as building edges in the point clouds are projected into the image space through the initial rational polynomial coefficient(RPC) parameters of the satellite image, and the iterative closest point registration is carried out with the line elements in the satellite image, so as to achieve geometric registration by means of refining RPC parameters. In this method, the distance transformation model is used as the search table of the iterative closest point, which greatly improves the operation efficiency. Furthermore, the latest progressive robust solution strategy is adopted at the same time of the closest point iteration, so as to ensure the robustness of registration in the case of too much noise.Registration experiments are carried out on GeoEye-2 data, Gaofen-7 data, WorldView-3 data with LiDAR point clouds data. Results The results prove that the proposed method can achieve a registration accuracy of 0.4—0.7 m on three kinds of images by using the ground control points which are accurately measured and the operator's internal control points as checkpoints. Conclusions It is significantly better than the strategy of mapping point clouds to 2D image and registering through multi-mode matching. |
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