Tree Species Recognition Using Combined Attention and ResNet for Unmanned Aerial Vehicle Images

Zhiyang Xu; Qiao Chen; Yongfu Chen

doi:10.3788/LOP212527

Laser & Optoelectronics Progress, Volume. 60, Issue 2, 0210004(2023)

Tree Species Recognition Using Combined Attention and ResNet for Unmanned Aerial Vehicle Images

Zhiyang Xu^1,2,3, Qiao Chen^1,2、*, and Yongfu Chen^1,2

Author Affiliations

¹Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

²Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

³East China Inventory and Planning Institute, National Forestry and Grassland Administration, Hangzhou 310019, Zhejiang , China

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Figures & Tables(8)

Fig. 1. ECA mechanism block

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Fig. 2. Basic unit of ECA-ResNet

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Fig. 3. Overall structure of proposed network

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Fig. 4. Results of single tree crown segmentation and recognition in five test circular samples

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Table 1. Training and independent test results of the model in single-tree crown image with different patch sizes

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Table 1. Training and independent test results of the model in single-tree crown image with different patch sizes

Scheme	Optimizer	Training data			Validation data			Independent test data
Scheme	Optimizer	Accuracy /%	Kappa	Loss	Accuracy /%	Kappa	Loss	Accuracy /%	Kappa
I（variable size）	SGD	97.98	0.9638	0.0973	95.73	0.9449	0.1929	77.27	0.7056
II（32×32 pixel）	SGD	93.25	0.9131	0.2711	86.75	0.8293	0.4920	68.18	0.5966
III（64×64 pixel）	SGD	98.98	0.9869	0.1012	96.60	0.9595	0.1678	85.61	0.8140
IV（96×96 pixel）	SGD	97.98	0.9769	0.0553	95.14	0.9388	0.1838	81.82	0.7629
V（128×128 pixel）	Adam	95.19	0.9381	0.2000	96.15	0.9505	0.1453	79.55	0.7332

Table 2. Confusion matrix of independent test dataset

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Table 2. Confusion matrix of independent test dataset

Recognized species	Ground true species
Recognized species	Alnus	Other broad-leaves	Cunninghamia	Liriodendron	Pinus
Alnus	26	1	0	0	0
Other broad-leaves	0	18	4	2	2
Cunninghamia	1	3	38	1	1
Liriodendron	0	0	1	15	0
Pinus	2	0	1	0	16
PA /%	89.66	81.82	86.36	83.33	84.21
UA /%	96.30	69.23	86.36	93.75	84.21

Table 3. Performance comparison of different models (single tree crown image clip dataset with 64×64 pixel)

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Table 3. Performance comparison of different models (single tree crown image clip dataset with 64×64 pixel)

Network	Training data			Validation data			Independent test data
Network	Accuracy /%	Kappa	Loss	Accuracy /%	Kappa	Loss	Accuracy /%	Kappa
VGG16	95.38	0.9405	0.1783	94.23	0.9258	0.2090	75.00	0.6804
ResNet18	94.36	0.9251	0.1529	93.80	0.9203	0.3176	77.27	0.7036
ResNet34	95.84	0.9464	0.2126	93.80	0.9201	0.2722	79.55	0.7356
ResNet50	96.35	0.9442	0.1505	94.80	0.9301	0.2024	80.30	0.7452
ResNet101	96.69	0.9602	0.1278	94.44	0.9284	0.2348	69.70	0.6130
ResNet152	96.41	0.9567	0.1229	94.87	0.9339	0.2264	65.91	0.5641
resnext50_32x4d	96.77	0.9583	0.0999	94.44	0.9285	0.1845	72.73	0.6501
densenet121	96.40	0.9536	0.1544	93.16	0.9119	0.2911	73.48	0.6557
MobileNetV2	86.41	0.8254	0.4771	88.89	0.8571	0.3599	65.91	0.5555
SqueezeNet	84.01	0.7939	0.5726	86.75	0.8286	0.4139	68.94	0.5948
ECA-ResNet	98.98	0.9869	0.1012	96.60	0.9595	0.1678	85.61	0.8140

Table 4. Performance comparison of CNN model before and after improvement

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Table 4. Performance comparison of CNN model before and after improvement

Scheme

Operation

ECA

Training accuracy /%

Validation accuracy /%

Test accuracy /%

FLOPs /Gbit

Parameter

Speed /

（frame·s^-1）

I（ResNet50）

Before reduced

96.35

94.80

80.30

3.827

23518277

4.59

Before reduced

√

96.57

94.51

83.09

3.832

23518325

4.99

III（ECA-ResNet）

After reduced

√

98.98

96.60

85.61

3.015

19886697

5.45

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Zhiyang Xu, Qiao Chen, Yongfu Chen. Tree Species Recognition Using Combined Attention and ResNet for Unmanned Aerial Vehicle Images[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0210004

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Paper Information

Category: Image Processing

Received: Sep. 14, 2021

Accepted: Nov. 10, 2021

Published Online: Jan. 6, 2023

The Author Email: Qiao Chen (Chengqiqo@163.com)

DOI:10.3788/LOP212527

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology