Tree-Species Identification of Multisource Remote-Sensing Data using Improved 3D-CNN

Xusheng Li; Donghua Chen; Saisai Liu; Naiming Zhang; Hu Li

doi:10.3788/LOP57.242804

Laser & Optoelectronics Progress, Volume. 57, Issue 24, 242804(2020)

Tree-Species Identification of Multisource Remote-Sensing Data using Improved 3D-CNN

Xusheng Li¹, Donghua Chen^2,3、*, Saisai Liu³, Naiming Zhang⁴, and Hu Li^2、*

Author Affiliations

¹College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China

²School of Geography and Tourism, Anhui Normal University, Wuhu, Anhui 241000, China

³College of Computer and Information Engineering, Chuzhou University, Chuzhou, Anhui 239000, China

⁴College of Geography and Tourism, Xinjiang Normal University, Urumqi, Xinjiang 830001, China

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Figures & Tables(16)

Fig. 1. Principle comparison between 3D-CNN and 2D-CNN. (a) 2D-CNN; (b) 3D-CNN

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Fig. 2. Diagram of network structure. (a) Flat network; (b) ResNet

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Fig. 3. Structure of 3D-RCNN

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Fig. 4. Distribution of study area and samples

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Fig. 5. Correlation matrix of characteristic factors. (a) Spectral feature; (b) texture feature; (c) vegetation index feature

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Fig. 6. Sample expansion by inner ring rotation

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Fig. 7. Influence of number of convolution units on test time and overall accuracy

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Fig. 8. Influence of step size on test time and overall accuracy

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Fig. 9. Tree species distribution. (a) Forest inventory; (b) algorithm extraction

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Table 1. Basic information of remote-sensing image data
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Table 1. Basic information of remote-sensing image data
Sensor Product number Latitude and longitude of center Imaging time Cloud cover /%
GF-5 AHIS 45157 118.02°E,32.30°N 2019-05-22
GF-6 PMS 1119873930 117.90°E,32.10°N 2019-05-01 <5

Table 2. Number of samples

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Table 2. Number of samples

Tree species	Measured samples	Expanded samples	Regular samples	Sample set
Quercus acutissima	64	100	231	3696
Celtissinensis	72	100	197	3152
Dalbergiahupeana	66	100	147	2352
Pinus massoniana	70	100	292	4672
Pinus elliottii	77	100	206	3296
Cunninghamia lanceolate	78	100	175	2800
Others	52	60	155	2480

Table 3. Influence of input pixel size on operation time and overall accuracy
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Table 3. Influence of input pixel size on operation time and overall accuracy
P Training time /s Test time /s OA /%
3 879.22 9.12 83.22
15 1020.55 11.22 83.95
17 1159.53 14.71 84.87
19 1362.99 33.53 84.90
21 1505.88 52.12 84.73
23 1817.58 88.60 84.85

Table 4. Influence of convolution kernel size on operation time and overall accuracy
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Table 4. Influence of convolution kernel size on operation time and overall accuracy
Q Test time /s OA /%
3×3×3 76.20 90.27
5×5×5 80.10 90.79
7×7×7 88.01 90.28
9×9×9 105.61 90.52

Table 5. Influence of learning rate on convergence rate and overall accuracy
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Table 5. Influence of learning rate on convergence rate and overall accuracy
S Number of iterations OA /%
0.0001 986 90.56
0.0002 776 90.92
0.0003 602 91.47
0.0004 474 91.69
0.0005 522 91.22
0.0006 439 90.03
0.0007 659 89.87
0.0008 755 90.21
0.0009 710 90.42
0.0010 928 89.15

Table 6. Classification accuracy evaluation matrix of each algorithm

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Table 6. Classification accuracy evaluation matrix of each algorithm

Algorithm	Parameter	Species 1	Species 2	Species 3	Species 4	Species 5	Species 6	Species 7
	UA /%	90.29	88.53	93.85	97.14	93.24	90.26	91.63
3D-RCNN	PA/%	95.63	92.26	97.46	86.97	92.76	89.57	94.96
	OA /%				91.72
	Kappa				0.849
	UA /%	80.72	87.51	90.23	88.76	87.57	87.69	81.11
3D-CNN	PA/%	90.12	83.66	87.55	84.84	86.89	80.19	82.68
	OA /%				85.65
	Kappa				0.820
	UA /%	87.50	77.50	92.50	95.00	72.50	82.50	91.67
SVM	PA /%	94.59	83.78	94.87	86.36	72.50	86.84	75.86
	OA /%				85.22
	Kappa				0.827

Table 7. Accuracy verification of tree species area

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Table 7. Accuracy verification of tree species area

Parameter	Species 1	Species 2	Species 3	Species 4	Species 5	Species 6	Species 7
Statistical area /km²	7.815	10.418	1.574	4.227	4.754	1.708	4.721
Identified area /km²	7.156	9.266	1.590	4.350	5.047	1.677	3.866
RA /%	91.57	88.94	99.00	97.08	93.83	98.15	81.89
Average RA /%				92.92

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Xusheng Li, Donghua Chen, Saisai Liu, Naiming Zhang, Hu Li. Tree-Species Identification of Multisource Remote-Sensing Data using Improved 3D-CNN[J]. Laser & Optoelectronics Progress, 2020, 57(24): 242804

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