基于植被指数限制分水岭算法的机载激光点云建筑物提取

Building Extraction from Airborne Laser Point Cloud Using NDVI Constrained Watershed Algorithm

建筑物提取在建筑物重建和城市管理中起着重要的作用。利用基于植被指数限制的分水岭算法分割机载激光雷达点云,并利用一定的规则识别建筑物区域。对激光点云进行内插生成网格数据;利用植被指数限制的分水岭分割算法分割激光点云生成的数字表面模型数据,在分水岭淹没过程中引入植被指数可以较好地区分建筑物和植被区域;在区域相邻关系的基础上,利用一些准则(高程差值、尺寸和植被指数)识别建筑物区域。利用国际摄影测量与遥感学会基准数据中法伊英根测试区域对建筑提取结果进行评价,在像元级别,平均完整度、正确度和质量分别为89.2%、94.3%和84.7%;在对象级别,平均完整度、正确度和质量分别为81.8%、93.1%和76.9%;在物体面积大于50m2的对象级别,平均完整度、正确度和质量可以达到99.1%、100%和99.1%。

Building extraction plays an important role in building reconstruction and urban management. In this study, a normalized difference vegetation index （NDVI） constrained watershed segmentation algorithm is utilized to segment airborne LiDAR data, and certain criteria are used to discriminate building regions as follows. First, grid data is attained by the interpolation of LiDAR point clouds. Then, the NDVI constrained watershed segmentation algorithm is applied to segmenting the digital surface model data, which is generated from LiDAR. Further, NDVI is introduced in the flooding process of the watershed algorithm to separate the vegetation from the buildings. Finally, the building regions are identified through some of the criteria （elevation difference, size, and NDVI） according to the adjacency relationship of each region. The benchmark data of the International Society for Photogrammetry and Remote Sensing for Vaihingen are used to evaluate the building detection results. The average completeness, correctness, and quality are respectively 89.2%, 94.3%, and 84.7% at the pixel level and 81.8%, 93.1%, and 76.9% respectively at the object level. Moreover, for an object with area larger than 50 m2, the average completeness, correctness, and quality are 99.1%, 100%, and 99.1%, respectively.