Automated Extraction of Building Outlines From Airborne Laser Scanning Point Clouds
Automated Extraction of Building Outlines From Airborne Laser Scanning Point Clouds
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DOI：
10.1109/lgrs.2013.2258887
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发表时间：
2013-06
影响因子：
4.8
通讯作者：
Bisheng Yang;Wenxue Xu;Z. Dong
中科院分区：
文献类型：
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作者：
Bisheng Yang;Wenxue Xu;Z. Dong
从机载激光扫描(ALS)点云中自动提取建筑物轮廓一直是摄影测量、遥感和计算机视觉领域的研究热点。本文提出了一种基于标记点处理的ALS点云建筑物轮廓提取方法。首先，定义了建筑对象的吉布斯能量模型来描述建筑点。其次，在可逆跳跃马尔可夫链蒙特卡罗框架内对所定义的Gibbs能量模型进行采样，并通过模拟退火法对其进行优化以找到最优的能量配置。最后对检测到的建筑目标进行细化，消除误检测，并利用形态算子从检测到的建筑目标中提取出建筑物的轮廓。利用ISPRS提供的标准数据集验证了该方法的有效性。该方法从标准数据集中提取建筑物目标，像素级的平均完备率和正确率分别为87.3%和91.57%，目标级的平均完备率和正确率分别为77.6%(97.3%)和98.1%(97.9%)。
Automatic extraction of building outlines from airborne laser scanning (ALS) point clouds has been an active topic in the field of photogrammetry, remote sensing, and computer vision. In this letter, a marked point process method is implemented to extract building outlines from ALS point clouds. First, the Gibbs energy model of building objects is defined to describe the building points. Second, the defined Gibbs energy model is sampled within the framework of reversible-jump Markov chain Monte Carlo and optimized to find an optimal energy configuration by simulated annealing. Finally, the detected building objects are refined to eliminate false detections, and the outlines of buildings are derived from the detected building objects by morphological operators. The standard data set provided by ISPRS is used to verify the validity of the proposed method. The method extracted building objects from the standard data sets with an average completeness of 87.3% and correctness of 91.57% at the pixel level, and an average completeness of 77.6% (97.3%) and correctness of 98.1% (97.9%) at the object level.