激光与光电子学进展, 2020, 57 (20): 201005, 网络出版: 2020-10-17
航空影像辅助的机载LiDAR植被点云分类 下载: 787次
Classification of Airborne LiDAR Vegetation Piont Clouds Assisted by Aerial Images
图像处理 机载激光雷达 航空影像 融合 植被点云分类 image processing airborne light detection and ranging aerial images fusion vegetation point cloud classification
摘要
针对从非地面点云数据中难以自动分类植被和建筑物的问题,提出一种航空影像辅助的机载LiDAR(Light Detection and Ranging)植被点云分类方法。根据植被的光谱特征明显不同于其他地物这一特点,在生成数字正射影像的基础上,首先利用K均值(K-means)聚类算法对影像进行聚类和图像增强,然后将增强后的影像和对应区域的点云数据进行融合,最后通过影像处理结果对机载LiDAR植被点云进行分类。选取某城市的机载LiDAR植被点云数据和航空影像进行实验,定量分析结果显示所提方法的总分类精度为96.47%,Kappa系数为0.9248,该方法能够达到点云中植被自动分类的目的。
Abstract
Since it is difficult to automatically distinguish between vegetation and buildings from non-ground point cloud data, this research work proposes a method to automatically classify vegetation in airborne LiDAR (Light Detection and Ranging) point clouds, which is assisted by aerial image. Based on the fact that the spectral characteristics of vegetation are clearly different from other ground objects, digital orthophoto generation and K-means clustering algorithm are employed to cluster and enhance the images. Then, the enhanced image and the point cloud data of the corresponding area are fused. Finally, the airborne LiDAR vegetation point cloud data is classified using the image processing results. Experiments are carried out on airborne LiDAR vegetation point cloud data and aerial images of a particular city. Quantitative analysis results prove that total classification accuracy of the proposed method is 96.47%, and the Kappa coefficient is 0.9248. The introduced method can pave the way for automatic classification of the vegetation in LiDAR point clouds.
王果, 王强, 张振鑫, 徐棒, 赵光兴. 航空影像辅助的机载LiDAR植被点云分类[J]. 激光与光电子学进展, 2020, 57(20): 201005. Guo Wang, Qiang Wang, Zhenxin Zhang, Bang Xu, Guangxing Zhao. Classification of Airborne LiDAR Vegetation Piont Clouds Assisted by Aerial Images[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201005.