机载激光扫描数据聚类的进化方法
An Evolutionary Approach towards Clustering Airborne Laser Scanning Data
摘要：
在土地测量方面，由于采用了正射影像，并在稍后阶段采用了机载激光雷达激光扫描系统，地图的制作大大简化。虽然激光雷达系统的最初目的是确定地面海拔高度，但较新的全波系统提供了可用于分类地面覆盖类型和生成地图的额外信息。LiDAR产生的点云是巨大的多维数据集，需要按地面覆盖类别进行分组。我们提出了一种遗传算法，有助于这些数据集的分类，从而使它们可用于地图生成。一个关键特征是为主题量身定制的遗传算子和适应度函数。该算法相比，传统的k-means聚类。
英文摘要：
In land surveying, the generation of maps was greatly simplified with the introduction of orthophotos and at a later stage with airborne LiDAR laser scanning systems. While the original purpose of LiDAR systems was to determine the altitude of ground elevations, newer full wave systems provide additional information that can be used on classifying the type of ground cover and the generation of maps. The LiDAR resulting point clouds are huge, multidimensional data sets that need to be grouped in classes of ground cover. We propose a genetic algorithm that aids in classifying these data sets and thus make them usable for map generation. A key feature are tailor-made genetic operators and fitness functions for the subject. The algorithm is compared to a traditional k-means clustering.
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