刘伟玉, 万一, 张永军, 姚永祥, 刘欣怡, 史立松. 基于相位均匀卷积的LiDAR深度图与航空影像高效匹配方法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(8): 1309-1317. DOI: 10.13203/j.whugis20210524
引用本文: 刘伟玉, 万一, 张永军, 姚永祥, 刘欣怡, 史立松. 基于相位均匀卷积的LiDAR深度图与航空影像高效匹配方法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(8): 1309-1317. DOI: 10.13203/j.whugis20210524
LIU Weiyu, WAN Yi, ZHANG Yongjun, YAO Yongxiang, LIU Xinyi, SHI Lisong. An Efficient Matching Method of LiDAR Depth Map and Aerial Image Based on Phase Mean Convolution[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1309-1317. DOI: 10.13203/j.whugis20210524
Citation: LIU Weiyu, WAN Yi, ZHANG Yongjun, YAO Yongxiang, LIU Xinyi, SHI Lisong. An Efficient Matching Method of LiDAR Depth Map and Aerial Image Based on Phase Mean Convolution[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1309-1317. DOI: 10.13203/j.whugis20210524

基于相位均匀卷积的LiDAR深度图与航空影像高效匹配方法

An Efficient Matching Method of LiDAR Depth Map and Aerial Image Based on Phase Mean Convolution

  • 摘要: 多源影像匹配主要受到非线性强度差异、对比度差异及局部区域结构特征不显著等问题的干扰,而机载激光雷达(light detection and ranging,LiDAR)深度图与航空影像由于纹理特征之间的显著差异导致部分结构特征缺失更为严重,所造成的相位极值突变进一步增加了匹配难度。因此,提出一种基于相位均匀卷积描述子的方法来实现LiDAR深度图与航空影像之间的高效匹配。在影像特征匹配阶段,首先对相位一致性模型进行扩展,构造相位均匀能量卷积方程,求解得到均匀卷积序列与相位最大标签图,建立一种相位均匀能量卷积直方图(histogram of phase mean energy convolution,HPMEC);然后采用最近邻匹配算法完成初始匹配,并利用快速边缘化样本共识进行粗差剔除;最后基于线程池并行策略,通过划分重叠格网对影像进行分块匹配。将多组具有不同地物覆盖类型的LiDAR深度图与航空影像作为数据集,分别与位置尺度方向-尺度不变特征转换(position scale orientation- scale invariant feature transform,PSO-SIFT)、Log-Gabor直方图描述(Log-Gabor histogram descriptor,LGHD)、辐射变化强度特征转换(radiation-variation insensitive feature transform,RIFT)和绝对相位一致性梯度直方图(histogram of absolute phase consistency gradients,HAPCG)等方法进行对比实验。结果表明,在LiDAR深度图与航空影像匹配中,HPMEC方法性能明显优于其他4种方法,其平均运行时间是PSO-SIFT的13.3倍、LGHD的10.9倍、HAPCG的10.4倍和RIFT的7.0倍;平均正确匹配点数显著高于其他4种方法;均方根误差在1.9像素以内,略优于其他4种方法。HPMEC方法在LiDAR深度图与航空影像中能够实现高效、稳健匹配。

     

    Abstract:
      Objectives  Multi-source image matching is primarily disturbed by nonlinear intensity difference, contrast difference and inconspicuous regional structure features, while the significant differences of texture features result in lack of part structure seriously between light detection and ranging(LiDAR)depth map and aerial image, and this problem causes a mutation in the phase extremum, which further increases the difficulty of matching.
      Methods  In this paper, a method of efficient matching of LiDAR depth map and aerial image based on phase mean convolution is proposed. In the image feature matching stage, a histogram of phase mean energy convolution(HPMEC) is established, which extended the phase consistency model in order to solve a mean convolution sequence and phase maximum label map by constructing phase mean energy convolution equation. Then the nearest neighbor matching algorithm was completed the initial match and marginalizing sample consensus plus was used to remove outliers. Based on the thread pool parallel strategy, the images were matched by dividing the overlapping grid. Multiple sets of LiDAR depth map and aerial image with different types of ground coverage are used to as dataset to experiment with position scale orientation-scale invariant feature transform (PSO-SIFT), Log-Gabor histogram descriptor (LGHD), radiation-variation insensitive feature transform (RIFT) and histogram of absolute phase consistency gradients (HAPCG) methods respectively.
      Results  The results show that the performance of HPMEC method is superior to the other four methods in the matching of LiDAR depth map and aerial image, the average running time is 13.3 times of PSO-SIFT, 10.9 times of LGHD, 10.4 times of HAPCG and 7.0 times of RIFT, at the same time the average correct matching points are significantly higher than the other four methods, the root mean square error is lightly better than the other four methods within 1.9 pixels.
      Conclusions  The proposed HPMEC method could achieve efficient and robust matching between LiDAR depth map and aerial image.

     

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