LiDAR Data Classification Based on Dilated Convolution Capsule Network

Aili Wang; Yuxiao Zhang; Haibin Wu; Kaiyuan Jiang; Yuji Iwahori

doi:10.3788/CJL202148.1110003

Journals >Chinese Journal of Lasers >Volume 48 >Issue 11 >Page 1110003 > Article

Chinese Journal of Lasers
Vol. 48, Issue 11, 1110003 (2021)

LiDAR Data Classification Based on Dilated Convolution Capsule Network

Aili Wang¹, Yuxiao Zhang¹, Haibin Wu^1、*, Kaiyuan Jiang¹, and Yuji Iwahori²

Author Affiliations

¹Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China

²Department of Computer Science, Chubu University, Aichi 487- 8501, Japan

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DOI: 10.3788/CJL202148.1110003 Cite this Article Set citation alerts

Aili Wang, Yuxiao Zhang, Haibin Wu, Kaiyuan Jiang, Yuji Iwahori. LiDAR Data Classification Based on Dilated Convolution Capsule Network[J]. Chinese Journal of Lasers, 2021, 48(11): 1110003 Copy Citation Text

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Fig. 1. Structure of dilated convolution capsule network

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Fig. 2. Residual block structure

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Fig. 3. Dilated convolution with different dilation rates. (a) r=1; (b) r=2; (c) r=3

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Fig. 4. Odd-even mixed dilation rates. (a) r=5; (b) r=2; (c) r=1

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Fig. 5. Capsule network structure

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Fig. 6. Capsule structure

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Fig. 7. DSM and groundtruth map of Bayview Park dataset. (a) DSM; (b) groundtruth map

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Fig. 8. DSM and groundtruth map of Recology dataset. (a) DSM; (b) groundtruth map

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Fig. 9. Dilation rate distribution of different datasets. (a) Bayview Park dataset; (b) Recology dataset

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Fig. 10. Classification results of Bayview Park dataset. (a) Groundtruth map; (b) SVM; (c) Random Forest; (d) CNN; (e) CapsNet; (f) ResNet; (g) Dilated-ResNet; (h) ResCapsNet; (i) DCCN

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Fig. 11. Classification results of Recology dataset. (a) Groundtruth map; (b) SVM; (c) Random Forest; (d) CNN; (e) CapsNet; (f) ResNet; (g) Dilated-ResNet; (h) ResCapsNet; (i) DCCN

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Layer name	Output size	ResNet-34 parameter setting
Conv 1	18×18	3×3, 16, stride 1
Conv2_x	18×18	3×3 max pooling, stride 2 $(\begin{array}{l} 3 \times 3 \\ 3 \times 3 \end{array} \begin{matrix} , 16 \end{matrix})$ ×3
Conv3_x	18×18	$(\begin{array}{l} 3 \times 3 \\ 3 \times 3 \end{array} \begin{matrix} , 28 \end{matrix})$ ×4
Conv4_x,distribution ofdilation rate is[1,2,5]	18×18	$(\begin{array}{l} 3 \times 3 \\ 3 \times 3 \end{array} \begin{matrix} , 40 \end{matrix})$ ×6
Conv5_x,distribution ofdilation rate is[1,2,5]	18×18	$(\begin{array}{l} 3 \times 3 \\ 3 \times 3 \end{array} \begin{matrix} , 52 \end{matrix})$ ×3
Output	9×9	average pooling, stride 2

Table 1. ResNet-34 model parameters

Training sample	Index	400	500	600	700
	OA /%	72.30±2.07	75.27±1.36	75.36±2.02	76.75±0.76
SVM	AA /%	76.98±1.42	78.50±2.10	78.86±1.12	81.20±2.27
	100K	65.22±1.79	66.36±0.95	67.65±1.72	69.12±2.23
	OA /%	86.56±0.75	87.34±0.62	88.23±0.34	90.56±0.47
Random Forest	AA /%	88.94±1.52	89.12±0.23	89.45±0.38	90.14±0.73
	100K	82.66±0.24	83.61±0.38	84.26±0.39	86.73±0.64
	OA /%	87.23±2.01	88.12±0.96	88.52±0.43	90.72±1.69
CNN	AA /%	88.70±1.13	89.63±2.71	89.96±1.65	90.23±0.68
	100K	83.26±1.45	85.25±1.46	86.23±1.78	86.72±2.34
	OA /%	85.43±1.12	87.25±0.89	90.07±1.08	90.73±0.36
CapsNet	AA /%	84.26±1.78	88.26±1.38	91.14±1.35	91.86±1.52
	100K	81.21±0.81	83.19±0.69	86.81±1.45	86.92±1.22
	OA /%	90.25±1.73	92.16±1.26	93.26±1.21	94.59±1.20
ResNet	AA /%	91.53±1.38	93.23±0.81	94.25±1.06	95.86±1.25
	100K	87.15±1.49	89.46±1.48	91.26±1.77	93.49±1.28
	OA /%	91.32±0.45	93.16±0.75	93.89±0.34	95.84±1.25
Dilated-ResNet	AA /%	92.67±0.76	94.09±1.06	95.27±0.87	96.42±1.08
	100K	88.46±1.05	91.58±0.57	92.65±0.65	94.15±1.34
	OA /%	93.15±0.52	94.79±0.41	94.59±0.73	96.42±0.71
ResCapsNet	AA /%	94.27±0.43	95.42±0.90	96.03±0.75	97.01±1.07
	100K	90.49±1.00	92.48±0.47	93.23±0.75	94.99±1.17
	OA /%	93.48±0.39	94.51±047	95.45±0.60	97.07±0.54
DCCN	AA /%	94.97±0.44	95.39±0.61	95.90±0.74	97.70±0.20
	100K	91.36±0.57	92.79±0.42	94.02±0.72	96.14±0.71

Table 2. Classification results of different training samples on Bayview Park dataset

Training sample	Index	400	500	600	700
	OA /%	72.68±1.89	76.83±0.26	76.94±2.25	77.25±0.86
SVM	AA /%	77.20±1.22	78.69±1.87	78.73±1.08	81.28±2.33
	100K	67.22±1.79	68.42±0.98	68.82±1.59	69.78±2.24
	OA /%	85.24±1.25	87.33±0.74	89.20±1.88	91.75±1.00
Random Forest	AA /%	88.36±1.92	89.85±3.06	90.27±1.26	91.29±1.41
	100K	82.25±0.88	86.26±1.57	86.59±1.97	89.22±1.34
	OA /%	86.26±1.48	88.24±0.98	90.52±0.68	92.73±1.86
CNN	AA /%	89.16±2.84	90.15±0.29	90.67±1.24	92.44±2.34
	100K	83.18±1.59	86.78±0.67	86.83±0.88	90.15±2.17
	OA /%	80.73±1.07	84.92±1.67	86.75±0.43	90.26±1.24
CapsNet	AA /%	81.93±1.94	86.29±1.08	86.95±1.09	91.26±1.99
	100K	76.79±1.68	81.67±0.85	83.92±0.76	88.37±1.52
	OA /%	90.58±1.92	93.56±1.42	95.57±0.76	95.83±0.99
ResNet	AA /%	88.86±2.14	94.52±1.22	94.33±1.32	95.36±1.88
	100K	88.84±2.34	93.04±1.70	94.98±0.84	95.24±1.27
	OA /%	92.07±0.98	93.75±0.34	94.87±0.54	95.89±0.76
Dilated-ResNet	AA /%	93.76±0.67	94.88±0.96	95.98±1.25	96.34±0.38
	100K	90.67±0.34	92.39±1.07	94.77±1.15	95.09±0.47
	OA /%	93.44±1.21	94.35±1.23	96.07±0.48	96.31±0.73
ResCapsNet	AA /%	94.63±0.17	95.22±060	97.16±1.15	97.30±0.17
	100K	91.29±0.70	93.79±0.99	95.33±0.63	95.43±0.31
	OA /%	94.01±0.36	94.99±0.96	96.42±0.63	96.98±0.76
DCCN	AA /%	94.97±0.58	95.67±0.46	97.49±0.69	97.77±0.78
	100K	93.28±0.34	94.35±0.99	96.06±0.70	96.41±0.90

Table 3. Classification results of different training samples on Recology dataset

Classe	SVM	RandomForest	CNN	CapsNet	ResNet	Dilated-ResNet	ResCapsNet	DCCN
Building1	82.20±3.72	95.24±3.75	93.53±1.50	94.28±1.45	98.26±1.55	98.96±1.04	99.36±0.64	99.74±0.26
Building2	84.59±2.58	98.83±1.03	92.72±1.08	95.22±2.03	99.64±0.36	99.78±0.22	99.79±0.21	99.54±0.46
Building3	91.36±4.96	100	92.90±1.50	93.32±1.93	99.57±0.43	99.68±0.32	100	99.69±0.31
Road	81.60±4.43	82.59±6.35	91.36±1.58	94.90±1.20	96.38±2.65	97.09±2.83	98.12±1.22	98.82±1.18
Trees	83.78±1.69	90.43±1.20	86.79±1.82	92.82±1.79	97.65±0.82	98.06±0.76	98.67±0.88	98.39±0.36
Soil	62.23±2.27	86.78±0.52	85.59±1.71	83.56±0.80	86.95±1.62	87.47±1.52	89.32±2.06	90.98±1.39
Seawater	86.79±2.48	84.21±1.25	90.75±2.74	85.53±1.23	91.18±2.87	91.88±2.65	93.47±2.30	94.79±1.17

Table 4. Classification results of each class for 700 samples on Bayview Park dataset unit:%

Classe	SVM	RandomForest	CNN	CapsNet	ResNet	Dilated-ResNet	ResCapsNet	DCCN
Building1	71.78±1.21	92.56±2.78	98.78±1.22	92.29±1.29	98.75±1.25	98.86±1.14	98.06±1.74	98.97±0.32
Building2	64.54±1.77	94.67±3.75	96.52±1.42	94.45±1.36	99.24±0.76	99.02±0.98	99.46±0.54	99.82±0.18
Building3	92.78±1.20	94.24±1.44	94.16±1.25	93.47±1.35	98.42±1.58	98.56±1.44	98.41±1.16	98.04±0.96
Building4	90.39±2.47	97.88±0.24	97.55±1.36	95.16±1.01	95.41±1.72	97.52±2.31	99.67±0.33	98.24±1.76
Building5	86.26±1.73	96.30±2.77	97.48±2.38	98.06±1.94	99.86±0.14	99.07±0.93	98.72±1.28	99.55±0.45
Building6	71.52±1.62	95.27±1.36	94.37±1.07	87.47±1.59	96.85±2.34	97.26±1.09	98.35±1.65	97.06±2.83
Building7	88.37±2.74	97.26±2.74	97.48±1.95	95.86±2.15	92.43±2.53	95.97±1.75	98.79±1.21	99.51±0.49
Trees	86.88±1.24	95.67±0.21	95.64±1.24	90.14±0.35	97.48±1.85	96.47±1.66	95.54±1.31	97.32±1.55
Parking Lot	62.77±1.92	76.21±0.18	77.86±1.87	83.39±0.14	89.45±1.75	89.29±1.06	89.28±1.27	91.31±0.58
Soil	81.83±3.22	73.16±0.32	73.29±1.72	75.41±1.45	88.69±2.47	90.66±2.54	95.68±2.43	96.92±3.08
Grass	97.77±1.37	98.26±1.25	97.26±1.58	98.26±1.42	92.54±2.46	93.48±2.49	95.52±2.61	99.61±0.39

Table 5. Classification results of each class for 700 samples on Recology dataset unit:%

Dataset	Network	Traintime /s	Testtime /s	OA /%
	ResNet	125.15	1.82	94.51
	Dilated-ResNet	195.34	2.76	95.67
BayviewPark	CapsNet	99.58	2.18	90.73
	ResCapsNet	343.85	3.00	96.42
	DCCN	518.65	3.75	97.07
	ResNet	196.34	2.67	95.83
	Dilated-ResNet	257.98	2.98	96.36
Recology	CapsNet	94.27	1.39	90.26
	ResCapsNet	428.69	3.34	96.31
	DCCN	586.55	3.96	96.98

Table 6. Comparison of calculation time for 700 samples on Recology dataset and Bayview Park dataset

Aili Wang, Yuxiao Zhang, Haibin Wu, Kaiyuan Jiang, Yuji Iwahori. LiDAR Data Classification Based on Dilated Convolution Capsule Network[J]. Chinese Journal of Lasers, 2021, 48(11): 1110003

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