Building Extraction from Airborne LiDAR Data Based on Multi-Constraints Graph Segmentation
Building Extraction from Airborne LiDAR Data Based on Multi-Constraints Graph Segmentation
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DOI：
10.3390/rs13183766
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Z. Hui;Zhuoxuan Li;P. Cheng;Yao Ziggah Yevenyo;JunLin Fan
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文献类型：
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作者：
Z. Hui;Zhuoxuan Li;P. Cheng;Yao Ziggah Yevenyo;JunLin Fan
从机载光探测和测距（LiDAR）点云中提取建筑物是数字城市建设过程中的重要一步。虽然现有的建筑物提取方法在简单的城市环境中表现良好，但当遇到建筑物形状不规则或建筑物尺寸变化的复杂城市环境时，这些方法无法取得满意的建筑物提取结果。为了解决这些挑战，本文提出了一种基于多约束图分割的机载激光雷达数据建筑物提取方法。该方法主要通过多约束图分割将基于点的建筑物提取转换为基于对象的建筑物提取。根据不同物体基元的空间几何特征导出初始提取的建筑点。最后，提出了一种多尺度渐进增长优化方法来恢复一些遗漏的建筑点，提高建筑提取的完整性。使用国际摄影测量和遥感协会（ISPRS）提供的三个数据集对所提出的方法进行了测试和验证。实验结果表明，该方法能够达到最佳的建筑物提取效果。研究还发现，无论是平均质量还是平均 F1 分数，该方法都优于其他十种研究的建筑物提取方法。
Building extraction from airborne Light Detection and Ranging (LiDAR) point clouds is a significant step in the process of digital urban construction. Although the existing building extraction methods perform well in simple urban environments, when encountering complicated city environments with irregular building shapes or varying building sizes, these methods cannot achieve satisfactory building extraction results. To address these challenges, a building extraction method from airborne LiDAR data based on multi-constraints graph segmentation was proposed in this paper. The proposed method mainly converted point-based building extraction into object-based building extraction through multi-constraints graph segmentation. The initial extracted building points were derived according to the spatial geometric features of different object primitives. Finally, a multi-scale progressive growth optimization method was proposed to recover some omitted building points and improve the completeness of building extraction. The proposed method was tested and validated using three datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that the proposed method can achieve the best building extraction results. It was also found that no matter the average quality or the average F1 score, the proposed method outperformed ten other investigated building extraction methods.