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首页> 外文期刊>International journal of image and data fusion >Information fusion of Lidar range and intensity data for automatic building recognition
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Information fusion of Lidar range and intensity data for automatic building recognition

机译：激光雷达测距和强度数据的信息融合，可自动识别建筑物

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摘要

Lidar has proved to be a promising data source for various mapping and 3D modelling of buildings in urban areas. Therefore, many researchers have been trying to study and develop automatic building recognition algorithms based on Lidar data. But, according to the adjacency of buildings and other objects in urban areas, especially trees, the performance of obtained results from most of these algorithms is still dependent to several assumptions and simplifications. In this article, a multi-agent methodology has been proposed for automatic building recognition based on the decision level fusion of textural and spatial information extracted from Lidar range and intensity products. In this multi-agent methodology, two different groups of object recognition agents are defined for building and tree detection in parallel and the algorithm has two different operational levels based on the types of contextual information. In the first level, both object recognition agents decide about the types of objects in the study area based on textural information, and the candidates of building and tree regions will be generated. In the second operational level, building recognition and tree recognition agents perform some operations in macro level in order to modify the candidates of building and tree regions based on spatial information. The evaluation of obtained results confirms the high capabilities of proposed multi-agent algorithm to decrease the conflicts in the field of automatic building recognition in complex urban areas.
机译：事实证明，激光雷达是用于城市建筑的各种地图绘制和3D建模的有前途的数据源。因此，许多研究人员一直在尝试研究和开发基于激光雷达数据的自动建筑物识别算法。但是，根据城市中建筑物和其他对象（尤其是树木）的邻接，从大多数这些算法获得的结果的性能仍然取决于几个假设和简化。在本文中，基于从激光雷达距离和强度乘积提取的纹理和空间信息的决策级融合，提出了一种多智能体方法用于建筑物自动识别。在这种多主体方法中，为建筑物和树检测并行定义了两组不同的对象识别主体，并且该算法基于上下文信息的类型具有两个不同的操作级别。在第一级中，两个对象识别代理根据纹理信息决定研究区域中的对象类型，并生成建筑物和树木区域的候选对象。在第二操作级别中，建筑物识别和树木识别代理在宏级别执行一些操作，以便基于空间信息修改建筑物和树木区域的候选对象。对所获得结果的评估证实了所提出的多智能体算法在减少复杂城市地区建筑物自动识别领域中的冲突方面具有很高的能力。

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