Urban Tree Extraction Method Based on LiDAR Data and Orthophoto

Fan Xie; Fengbao Yang; hong Wei

doi:10.3788/LOP202259.0428005

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 4 >Page 0428005 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 4, 0428005 (2022)

Urban Tree Extraction Method Based on LiDAR Data and Orthophoto

Fan Xie¹, Fengbao Yang^1、*, and hong Wei²

Author Affiliations

¹School of Information and Communication Engineering, North University of China, Taiyuan , Shanxi 030051, China

²School of Systems Engineering, University of Reading, ReadingRG6 6AU, UK

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DOI: 10.3788/LOP202259.0428005 Cite this Article Set citation alerts

Fan Xie, Fengbao Yang, hong Wei. Urban Tree Extraction Method Based on LiDAR Data and Orthophoto[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0428005 Copy Citation Text

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Fig. 1. Flowchart of the proposed method

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Fig. 2. Part of the roof has a high NDVI

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Fig. 3. Vegetation has a low NDVI in shaded area

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Fig. 4. Tree extraction results. (a1)‒(f1) NIRRG images; (a2)‒(f2) provided tree real areas; (a3)‒(f3) tree areas extracted by proposed method; (a4) ‒ (f4) evaluation results

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Fig. 5. Typical classification error images

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Number	$P_{t}$	$P_{b}$	$U_{t}$	$U_{b}$	OA	F1
Fig. 4（a）	0.9341	0.9291	0.9032	0.9522	0.9312	0.9184
Fig. 4（b）	0.9578	0.9247	0.8434	0.9810	0.9345	0.8970
Fig. 4（c）	0.9206	0.9431	0.8550	0.9702	0.9371	0.8866
Fig. 4（d）	0.9093	0.9359	0.8627	0.9588	0.9277	0.8854
Fig. 4（e）	0.9209	0.9250	0.8810	0.9510	0.9235	0.9005
Fig. 4（f）	0.9090	0.9804	0.9214	0.9771	0.9660	0.9152

Table 1. Evaluation index results

Method	OA	F1	Method	OA	F1
Xiangda^［18］	0.898	0.908	Chisheng^［23］	0.878	0.811
HSN^［19］	0.888	0.883	Markus^［24］	0.892	0.823
HRNet^［20］	0.901	0.889	Nan^［25］	0.863	0.846
C3Net^［21］	0.913	0.903	Qiong^［26］	0.891	0.850
Marmanis^［22］	0.903	0.896	Proposed method	0.937	0.901

Table 2. Comparison of evaluation indexes of different methods

Fan Xie, Fengbao Yang, hong Wei. Urban Tree Extraction Method Based on LiDAR Data and Orthophoto[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0428005

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