Multi-level building change detection based on the DSM and DOM generated from UAV images
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摘要: 随着我国城镇化水平的不断提高,城镇建筑物日新月异,及时、准确地掌握城镇建筑物的变化信息对城镇管理、违章建筑查处及灾害评估有着重要意义。该文提出了一种联合无人机影像生成数字表面模型(digital surface model,DSM)和正射影像(digital orthophoto map,DOM)的多层次建筑物变化检测方法,主要包括4个步骤: ①对无人机影像生成的密集点云和DOM进行预处理,生成差分归一化DSM(differential normalized DSM,dnDSM)并提取植被区域; ②利用多层高差阈值提取候选变化区域,并在此过程中剔除植被及面积较小区域; ③对低层候选变化区域进行连通域分析,对于每个连通对象,利用其较高层的变化检测结果剔除低层中的误检测; ④统计每个变化对象的正、负高差值数量关系,确定变化类型。实验结果表明,该文方法不但能够保留较低高差阈值检测到的低矮变化建筑物,而且能够保证高大变化建筑物的正确性、完整性。Abstract: The continuous advancement of urbanization in China leads to frequently changing urban buildings. Hence, grasping the change information of urban buildings duly and accurately holds critical significance for urban management, investigation of unauthorized construction, and disaster assessment. This study proposed a multi-level building change detection method combined with the digital surface model (DSM) and digital orthophoto map (DOM) generated from unmanned aerial vehicle (UAV) images. The proposed method consists of four steps: ① The dense point cloud and DOM generated from UAV images were pre-processed to generate differential normalized DSM (dnDSM) and extract vegetation zones; ② Candidate change zones were extracted using multi-level height difference thresholds, with vegetation and smaller zones eliminated; ③ The connected component analysis was conducted for lower-level candidate change zones. For connected objects, their higher-level change detection results were used to eliminate false detection results in the lower level; ④ The quantitative relationship between positive and negative height difference values of change objects was statistically analyzed to determine the change types. As demonstrated by experimental results, the proposed method can retain the change information of low-rise buildings detected through the lower height difference thresholds while ensuring correct and complete change information of high-rise buildings.
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