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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (2) : 80-88     DOI: 10.6046/zrzyyg.2023001
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Multi-level building change detection based on the DSM and DOM generated from UAV images
CHAI Jiaxing1(), ZHANG Yunsheng1,2,3(), YANG Zhen1, CHEN Siyang1, LI Haifeng1
1. School of Geosciences and Info-physics, Central South University, Changsha 410012, China
2. Hunan Provincial Key Laboratory of Key Technology on Hydropower Development, Power China Zhongnan Engineering Co., Ltd., Changsha 410021, China
3. Key Laboratory of Ecological Environment Protection of Space Information Application of Henan, Zhengzhou 450046, China
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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.
Keywords building change detection      unmanned aerial vehicle image      digital surface model      digital orthophoto map     
ZTFLH:  TP79  
  P315  
Issue Date: 14 June 2024
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Jiaxing CHAI
Yunsheng ZHANG
Zhen YANG
Siyang CHEN
Haifeng LI
Cite this article:   
Jiaxing CHAI,Yunsheng ZHANG,Zhen YANG, et al. Multi-level building change detection based on the DSM and DOM generated from UAV images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 80-88.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023001     OR     https://www.gtzyyg.com/EN/Y2024/V36/I2/80
Fig.1  Workflow of the proposed method
Fig.2  Multi-level changed area extraction
时相 分辨率 航高/m 焦距/mm 数量
T1 4 000×6 000 约200 53.27 1 596
T2 7 952×5 304 约180 56 2 650
Tab.1  Collection information of experimental data
Fig.3-1  Experimental data
Fig.3-2  Experimental data
参数 本文取值
植被分割阈值 T f 0
高差阈值下限 T H m i n 1 m
高差阈值增量 Δ T H 0.5 m
高差阈值上限 T H m a x 12 m
层数选择参数 r 0.5
面积阈值下限 T S m i n 10 m2
面积阈值增量 Δ T S 4 m2
Tab.2  Parameters setting
Fig.4  Example of intermediate result
Fig.5  Building change detection result
方法 P r e c e s s i o n/% R e c a l l/% F 1/%
本文方法 95.23 93.57 94.40
T-dnDSM 95.37 78.35 86.03
DSM-GC 90.41 89.20 89.81
Tab.3  Building change detection accuracy
Fig.6  Building change type determination
Fig.7  Analysis of the variation of precision with parameters
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