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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 75-84     DOI: 10.6046/gtzyyg.2020289
Buildings extraction of GF-2 remote sensing image based on multi-layer perception network
LU Qi1(), QIN Jun1, YAO Xuedong2, WU Yanlan1,3(), ZHU Haochen1
1. School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430072, China
3. Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China
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The task of extracting buildings with high-resolution remote sensing image plays an important role in urban planning and urbanization. In view of the problems of existing deep learning extraction methods, for example, the shallow features can’t been used effectively and small target information is easily lost, this paper proposes a multi-level perceptual network. This network uses dense connection mechanism to fully extract feature information, and constructs parallel structure to retain spatial information of different feature resolution and enhance feature information of different depth and scale in order to reduce the loss of detail feature. At the same time, the ASPP module is used to obtain the information of different receptive fields and extract the deep architectural features at different scales. The experimental results show that the overall accuracy of the proposed method is 97.19%, intersection over union is 74.33% and theF1 score is 85.43% in the buildings extraction of GF-2 remote sensing image, all of which are higher than those of the traditional method and other deep learning methods. In addition, buildings with multi-source remote sensing images still have good extraction effect, which reflects the practicability of the method presented in this paper.

Keywords deep learning      remote sensing images      building extraction      multiscale feature fusion     
ZTFLH:  TP79  
Corresponding Authors: WU Yanlan     E-mail:;
Issue Date: 21 July 2021
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Xuedong YAO
Yanlan WU
Haochen ZHU
Cite this article:   
Qi LU,Jun QIN,Xuedong YAO, et al. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
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Fig.1  Flow chart of network structure
Fig.2  A denseblock with three convolutional layers
Fig.3  Atrous spatial pyramid pooling
Fig.4  Three feature fusion methods
Fig.5  Division scope of training area and testing area in Hefei
用途 样本制作及模型测试 泛化性验证
Tab.1  Image parameters
类别 合肥示例1 合肥示例2 天津示例1 天津示例2
Tab.2  image and label data of building
Fig.6  Result display
区域 OA IOU F1
合肥区域 96.52 72.60 84.45
天津区域 97.86 76.06 86.40
平均精度 97.19 74.33 85.43
Tab.3  Test accuracy(%)
方法名称 OA IOU F1
本文方法 97.19 74.33 85.43
最大似然法 58.68 35.13 50.17
支持向量机 84.03 45.47 62.46
面向对象 67.40 35.13 50.17
Tab.4  Comparison of extraction accuracy with traditional methods(%)
区域 遥感影像 标签 本文方法 最大似然法 支持向量机 面向对象
Tab.5  Comparison of extraction results with traditional methods
方法名称 OA IOU F1
本文方法 97.19 74.33 85.43
DenseNet 88.29 55.39 70.01
DeeplabV3+ 91.01 70.01 82.71
BiseNet 89.55 54.83 72.40
Tab.6  Comparison of extraction accuracy with classic deep learning network model(%)
区域 遥感影像 标签 本文方法 DenseNet DeeplabV3+ BiseNet
Tab.7  Comparison of extraction results with classic deep learning network model
Fig.7-1  Multisource remote sensing image test results
Fig.7-2  Multisource remote sensing image test results
影像类型 OA IOU F1
GF-1 82.75 60.32 75.25
GF-6 90.96 87.81 93.51
SV1 95.93 51.95 68.37
Tab.8  Test accuracy(%)
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