Semantic Segmentation of Synthetic Aperture Radar Images Based on U-Net and Capsule Network

Shaodi Jing; Lingjuan Yu; Yuehong Hu; Zezhou Yang; Zhongliang Lu; Xiaochun Xie

doi:10.3788/LOP202158.2010009

Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2010009(2021)

Semantic Segmentation of Synthetic Aperture Radar Images Based on U-Net and Capsule Network

Shaodi Jing¹, Lingjuan Yu^1、*, Yuehong Hu², Zezhou Yang¹, Zhongliang Lu¹, and Xiaochun Xie³

Author Affiliations

¹School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China

²Guangzhou Wayful Technology Development Co., Ltd., Guangzhou, Guangdong 510200, China

³School of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi 341000, China

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Figures & Tables(15)

Fig. 1. Structure and parameters of the U-Net

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Fig. 2. Structure and parameters of the capsule network

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Fig. 3. Network based on U-Net and capsule network for semantic segmentation of SAR image

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Fig. 4. San Francisco Bay data. (a) RGB image; (b) label

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Fig. 5. Part of training samples in the San Francisco Bay data set

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Fig. 6. Oberpfaffenhofen data. (a) RGB image; (b) label

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Fig. 7. Part of training samples in the Oberpfaffenhofen data set

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Fig. 8. Segmentation results of different methods on the San Francisco Bay data set. (a) Method 1; (b) method 2; (c) method 3; (d) method 4

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Fig. 9. Segmentation results of different methods on the Oberpfaffenhofen data set. (a) Method 1; (b) method 2; (c) method 3; (d) method 4

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Table 1. Segmentation performance of different methods on the San Francisco Bay data set unit: %
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Table 1. Segmentation performance of different methods on the San Francisco Bay data set unit: %
Method X_Precision X_Recall X_F1-score X_IOU
Method 1 87.46 95.72 91.40 84.17
Method 2 92.87 95.11 93.98 88.64
Method 3 89.31 96.30 92.67 86.35
Method 4 93.86 96.31 95.07 90.60

Table 2. Percentage improvement of the segmentation performance (San Francisco Bay data set) unit: %

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Table 2. Percentage improvement of the segmentation performance (San Francisco Bay data set) unit: %

Method		X_Precision	X_Recall	X_F1-score	X_IOU
Use transfer learning	Method 2 vs. method 1	6.19	-0.64	2.82	5.31
Use transfer learning	Method 4 vs. method 3	5.10	0.01	2.58	4.92
Use capsule network	Method 3 vs. method 1	2.12	0.61	1.39	2.59
Use capsule network	Method 4 vs. method 2	1.07	1.26	1.16	2.21

Table 3. Training time of different methods on the San Francisco Bay data set
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Table 3. Training time of different methods on the San Francisco Bay data set
Method Method 1 Method 2 Method 3 Method 4
Training time /s 16 13 17 14

Table 4. Segmentation performance of different methods on the Oberpfaffenhofen data set unit: %
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Table 4. Segmentation performance of different methods on the Oberpfaffenhofen data set unit: %
Method X_Precision X_Recall X_F1-score X_IOU
Method 1 87.60 91.50 89.51 81.01
Method 2 90.89 91.53 91.21 83.84
Method 3 89.16 93.07 91.07 83.61
Method 4 92.06 93.18 92.62 86.24

Table 5. Percentage improvement of the segmentation performance (Oberpfaffenhofen data set) unit: %

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Table 5. Percentage improvement of the segmentation performance (Oberpfaffenhofen data set) unit: %

Method		X_Precision	X_Recall	X_F1-score	X_IOU
Use transfer learning	Method 2 vs. method 1	3.76	0.03	1.90	3.49
Use transfer learning	Method 4 vs. method 3	3.25	0.12	1.70	3.15
Use capsule network	Method 3 vs. method 1	1.78	1.72	1.74	3.21
Use capsule network	Method 4 vs. method 2	1.29	1.80	1.55	2.86

Table 6. Training time of different methods on the Oberpfaffenhofen data set
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Table 6. Training time of different methods on the Oberpfaffenhofen data set
Method Method 1 Method 2 Method 3 Method 4
Training time /s 37 29 40 32

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Shaodi Jing, Lingjuan Yu, Yuehong Hu, Zezhou Yang, Zhongliang Lu, Xiaochun Xie. Semantic Segmentation of Synthetic Aperture Radar Images Based on U-Net and Capsule Network[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010009

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Paper Information

Category: Image Processing

Received: Nov. 20, 2020

Accepted: Jan. 2, 2021

Published Online: Oct. 13, 2021

The Author Email: Yu Lingjuan (lingjuanyusmile@163.com)

DOI:10.3788/LOP202158.2010009

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Segmentation performance of different methods on the San Francisco Bay data set unit: %

Table 1. Segmentation performance of different methods on the San Francisco Bay data set unit: %

Table 2. Percentage improvement of the segmentation performance (San Francisco Bay data set) unit: %

Table 2. Percentage improvement of the segmentation performance (San Francisco Bay data set) unit: %

Table 3. Training time of different methods on the San Francisco Bay data set

Table 3. Training time of different methods on the San Francisco Bay data set

Table 4. Segmentation performance of different methods on the Oberpfaffenhofen data set unit: %

Table 4. Segmentation performance of different methods on the Oberpfaffenhofen data set unit: %

Table 5. Percentage improvement of the segmentation performance (Oberpfaffenhofen data set) unit: %

Table 5. Percentage improvement of the segmentation performance (Oberpfaffenhofen data set) unit: %

Table 6. Training time of different methods on the Oberpfaffenhofen data set

Table 6. Training time of different methods on the Oberpfaffenhofen data set