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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (1) : 45-53     DOI: 10.6046/gtzyyg.2020162
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Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network
LIU Zhao(), ZHAO Tong(), LIAO Feifan, LI Shuai, LI Haiyang
Institute of Transportation Engineering and Geomatics, Department of Civil Engineering, Tsinghua University, Beijing 100084, China
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Abstract  

The extraction of urban built-up areas plays an important role in urban development planning. To find out the method of extracting remote sensing image urban built-up area based on convolutional neural network which can balance efficiency and recognition accuracy, the authors started with the principle of neural network structure and compared as well as analyzed the internal structure of multiple semantic segmentation networks. The semantic segmentation network was trained separately and the results were comparatively studied. The experimental result shows that the ShelfNet-50 network could ensure high recognition accuracy while training speed, achieved 77% foreground segmentation accuracy while training time was only 14 hours, and the result of ShelfNet-50 network prediction was also highly consistent with the corresponding remote sensing image data. The experiment confirms that ShelfNet-50 network can be applied to high-resolution remote sensing image urban built-up area extraction problems.
Keywords high-resolution remote sensing image      convolutional neural network      semantic segmentation      urban built-up area     
ZTFLH:  P237  
Corresponding Authors: ZHAO Tong     E-mail: liuz@tsinghua.edu.cn;zhaot18@mails.tsinghua.edu.cn
Issue Date: 18 March 2021
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Zhao LIU
Tong ZHAO
Feifan LIAO
Shuai LI
Haiyang LI
Cite this article:   
Zhao LIU,Tong ZHAO,Feifan LIAO, et al. Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network[J]. Remote Sensing for Land & Resources, 2021, 33(1): 45-53.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020162     OR     https://www.gtzyyg.com/EN/Y2021/V33/I1/45
Fig.1  ResNet basic structure
Fig.2  Deeplab v3 semantic segmentation network structure[17]
Fig.3  PSPNet semantic segmentation network structure[20]
Fig.4  ShelfNet semantic segmentation network structure[21]
Fig.5  Partial training data set
Fig.5-2  Partial training data set
网络 训练集平
均损失		 验证集平
均损失		 验证集平
均准确率		 mIOU 背景IOU 前景IOU 前20循环平
均前景IOU
Deeplab v3-50 19.91 12.15 87.90 82.21 94.30 70.13 60.35
Deeplab v3-101 18.92 12.37 87.00 81.65 94.19 69.11 58.05
PSPNet50 19.21 12.02 88.03 82.35 94.34 70.36 63.30
PSPNet101 18.33 12.22 87.92 82.27 94.32 70.23 63.27
ShelfNet50 18.01 12.48 89.19 82.75 94.33 71.16 63.60
ShelfNet101 21.17 13.15 86.82 81.34 94.08 68.60 58.23
Tab.1  Network early stage training accuracy comparison(%)
分类器/网络 训练集平
均损失/%		 验证集平
均损失/%		 验证集平
均准确率/%		 mIOU/% 背景IOU/% 前景IOU/% 51-80循环平
均前景IOU/%		 训练时
长/h
随机森林 — — 85.48 55.98 — — — —
支持向量机 — — 79.82 46.27 — — — —
Deeplab v3-50 13.85 10.13 90.65 85.38 95.30 75.46 74.74 48.92
Deeplab v3-101 12.78 9.68 90.88 85.70 95.41 75.98 75.68 68.35
PSPNet50 13.72 9.81 90.78 85.37 95.28 75.45 74.73 32.13
PSPNet101 10.95 8.66 92.44 87.25 95.88 78.63 78.01 51.86
ShelfNet50 11.54 9.08 92.38 86.71 95.65 77.76 77.05 14.02
ShelfNet101 12.05 9.31 91.60 86.12 95.49 76.94 76.89 15.63
Tab.2  Network final training accuracy comparison
Fig.6-1  ShelfNet50 network test set segmentation results
Fig.6-2  ShelfNet50 network test set segmentation results
Fig.7  ShelfNet50 network test set segmentation error
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