ABSTRACT

This paper introduces a method based on centroid clustering and contour analysis to extract area and height measurements on buildings from the 3D model generated by oblique images. The method comprises three steps: (1) extract the contour plane from the fused data of the digital surface model (DSM) and digital orthophoto map (DOM); (2) identify building contour clusters based on the number of centroids contained in each category determined by mean-shift centroid clustering; (3) remove the mis-identified contours in a given building contour cluster by a contour analysis and obtain the geometric information of the building using map algebra. The proposed approach was tested against four datasets. Compared with other results, the detection has effective completeness, correctness, quality, and higher geometric accuracy. The maximum average relative error of building height and area extraction is less than 8%. The method is fast for a large-scale collection of building attributes and improves the applicability of oblique photography in GIS.

摘要

本文介绍了一种基于质心聚类和轮廓分析的方法，从倾斜图像生成的 3D 模型中提取建筑物的面积和高度测量值。该方法包括三个步骤： (1)从数字地表模型(DSM)和数字正射影像图(DOM)的融合数据中提取等高平面；(2) 根据均值偏移质心聚类确定的每个类别中包含的质心数量识别建筑物轮廓聚类；(3)通过轮廓分析去除给定建筑物轮廓簇中错误识别的轮廓，并利用地图代数获得建筑物的几何信息。所提出的方法针对四个数据集进行了测试。与其他结果相比，该检测具有有效的完整性、正确性、质量和更高的几何精度。建筑高度和面积提取的最大平均相对误差小于8%。该方法对大规模建筑物属性的采集速度快，提高了倾斜摄影在GIS中的适用性。