International Institute for Geo-Information Science and Earth Observation, Hengelosestraat 99, P.O. Box 6, 7500 AA Enschede, The Netherlands.
Sensors (Basel). 2009;9(8):6101-18. doi: 10.3390/s90806101. Epub 2009 Jul 31.
With the increasing point densities provided by airborne laser scanner (ALS) data the requirements on derived products also increase. One major application of ALS data is to provide input for 3D city models. Modeling of roof faces, (3D) road and terrain surfaces can partially be done in an automated manner, although many such approaches are still in a development stage. Problems in automatic building reconstruction lie in the dynamic area between assumptions and reality. Not every object in the data appears as the algorithm expects. Challenges are to detect areas that cannot be reconstructed automatically. This paper describes our contribution to the field of building reconstruction by proposing a target based graph matching approach that can handle both complete and incomplete laser data. Match results describe which target objects appear topologically in the data. Complete match results can be reconstructed in an automated manner. Quality parameters store information on how the model fits to the input data and which data has not been used. Areas where laser data only partly matches with target objects are detected automatically. Four datasets are analyzed in order to describe the quality of the automatically reconstructed roofs, and to point out the reasons why segments are left out from the automatic reconstruction. The reasons why these areas are left out include lack of data information and limitations of our initial target objects. Potential improvement to our approach is to include likelihood functions to the existence of topological relations.
随着机载激光扫描仪 (ALS) 数据提供的点密度不断增加，对衍生产品的要求也在增加。ALS 数据的一个主要应用是为 3D 城市模型提供输入。屋顶面、（3D）道路和地形表面的建模可以部分自动完成，尽管许多此类方法仍处于开发阶段。自动建筑物重建中的问题在于假设和现实之间的动态区域。数据中并非每个物体都符合算法的预期。挑战在于检测无法自动重建的区域。本文通过提出一种基于目标的图匹配方法来描述我们在建筑物重建领域的贡献，该方法可以处理完整和不完整的激光数据。匹配结果描述了拓扑上在数据中出现的目标对象。完整的匹配结果可以自动重建。质量参数存储有关模型与输入数据拟合程度以及哪些数据未被使用的信息。自动检测到激光数据与目标对象部分匹配的区域。为了描述自动重建屋顶的质量，并指出从自动重建中排除的片段的原因，分析了四个数据集。这些区域被排除的原因包括缺乏数据信息和我们初始目标对象的局限性。改进我们方法的潜力是将似然函数纳入拓扑关系的存在中。
