Scene Classification of Optical Remote Sensing Images Based on Residual Networks

Peng Wang; Rui Liu; Xuejing Xin; Peidong Liu

doi:10.3788/LOP202158.0210001

Laser & Optoelectronics Progress, Volume. 58, Issue 2, 0210001(2021)

Scene Classification of Optical Remote Sensing Images Based on Residual Networks

Peng Wang^*, Rui Liu^*, Xuejing Xin, and Peidong Liu

Author Affiliations

School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300100, China

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Abstract Get PDF(in Chinese)

Figures & Tables(15)

Fig. 1. In-class diversity. (a) (b) (c) Church category; (d) (e) (f) railway station category

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Fig. 2. Between-class similarity. (a) (b) freeway versus runway; (c) (d) industrial area versus railway station; (e) (f) stadium versus train station

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Fig. 3. Shortcut connection of resnet

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Fig. 4. Network structure diagram

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Fig. 5. Graphic example of jump connection

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Fig. 6. UC Merced Land Use remote sensing image dataset. (a) Beach; (b) baseball field; (c) overpass

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Fig. 7. Google of SIRI-WHU sensing image dataset. (a) River; (b) pond; (c) harbor

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Fig. 8. NWPU-RESISC45 sensing image dataset. (a) Forest; (b) circular farmland; (c) river

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Fig. 9. UC Merced Land Use data set classification results

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Fig. 10. Google of SIRI-WHU data set classification results

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Fig. 11. NWPU-RESISC45 data set classification results

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Table 1. Introduction of experimental environment
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Table 1. Introduction of experimental environment
Experimental environment Environment configuration
Operating system Ubuntu 16.04
Software environment Python 2.7,pytorch 0.4.1
CPU Xeon(R).W-2123
Internal memory DDR4,32G

Table 2. Comparison of the classification results obtained for the UC Merced Land Use dataset unit: %
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Table 2. Comparison of the classification results obtained for the UC Merced Land Use dataset unit: %
Model OA
DCA^[7] 96.90
AlexNet+MSCP^[14] 96.70
SCCov^[16] 98.04
ResNet 96.70
Ours 99.76

Table 3. Comparison of the classification results obtained for the Google of SIRI-WHU dataset unit: %
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Table 3. Comparison of the classification results obtained for the Google of SIRI-WHU dataset unit: %
Model OA
SRSCNN^[17] 93.40
AlexNet+Softmax^[18] 95.63
AlexNet+SVM^[18] 95.83
ResNet 93.75
Ours 97.91

Table 4. Comparison of the classification results obtained for the NWPU-RESISC45 dataset unit: %
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Table 4. Comparison of the classification results obtained for the NWPU-RESISC45 dataset unit: %
Model OA
DCNN^[9] 89.22
VGG+MSCP^[14] 88.93
SCCov^[16] 89.30
ResNet 87.61
Ours 92.45

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Peng Wang, Rui Liu, Xuejing Xin, Peidong Liu. Scene Classification of Optical Remote Sensing Images Based on Residual Networks[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210001

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Paper Information

Category: Image Processing

Received: Jun. 16, 2020

Accepted: Jul. 1, 2020

Published Online: Jan. 5, 2021

The Author Email: Wang Peng (hebutwangpeng2019@163.com), Liu Rui (hebutwangpeng2019@163.com)

DOI:10.3788/LOP202158.0210001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Introduction of experimental environment

Table 1. Introduction of experimental environment

Table 2. Comparison of the classification results obtained for the UC Merced Land Use dataset unit: %

Table 2. Comparison of the classification results obtained for the UC Merced Land Use dataset unit: %

Table 3. Comparison of the classification results obtained for the Google of SIRI-WHU dataset unit: %

Table 3. Comparison of the classification results obtained for the Google of SIRI-WHU dataset unit: %

Table 4. Comparison of the classification results obtained for the NWPU-RESISC45 dataset unit: %

Table 4. Comparison of the classification results obtained for the NWPU-RESISC45 dataset unit: %