Urban Tree Extraction Method Based on LiDAR Data and Orthophoto

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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Table 1. Evaluation index results

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Table 1. Evaluation index results

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 2. Comparison of evaluation indexes of different methods

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Table 2. Comparison of evaluation indexes of different methods

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

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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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Paper Information

Category: Remote Sensing and Sensors

Received: Apr. 8, 2021

Accepted: May. 18, 2021

Published Online: Jan. 25, 2022

The Author Email: Fengbao Yang (yfengb@163.com)

DOI:10.3788/LOP202259.0428005

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology