Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network
文献类型：期刊论文
| 作者 | Liu, Wei2,3; Yang, MengYuan3; Xie, Meng3; Guo, Zihui3; Li, ErZhu3; Zhang, Lianpeng3; Pei, Tao2; Wang, Dong1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2019-12-02 |
| 卷号 | 11期号:24页码:18 |
| 关键词 | building extraction digital surface model unmanned aerial vehicle images chain full convolution neural network fusion |
| DOI | 10.3390/rs11242912 |
| 通讯作者 | Liu, Wei(liuw@jsnu.edu.cn) |
| 英文摘要 | Accurate extraction of buildings using high spatial resolution imagery is essential to a wide range of urban applications. However, it is difficult to extract semantic features from a variety of complex scenes (e.g., suburban, urban and urban village areas) because various complex man-made objects usually appear heterogeneous with large intra-class and low inter-class variations. The automatic extraction of buildings is thus extremely challenging. The fully convolutional neural networks (FCNs) developed in recent years have performed well in the extraction of urban man-made objects due to their ability to learn state-of-the-art features and to label pixels end-to-end. One of the most successful FCNs used in building extraction is U-net. However, the commonly used skip connection and feature fusion refinement modules in U-net often ignore the problem of feature selection, and the ability to extract smaller buildings and refine building boundaries needs to be improved. In this paper, we propose a trainable chain fully convolutional neural network (CFCN), which fuses high spatial resolution unmanned aerial vehicle (UAV) images and the digital surface model (DSM) for building extraction. Multilevel features are obtained from the fusion data, and an improved U-net is used for the coarse extraction of the building. To solve the problem of incomplete extraction of building boundaries, a U-net network is introduced by chain, which is used for the introduction of a coarse building boundary constraint, hole filling, and "speckle" removal. Typical areas such as suburban, urban, and urban villages were selected for building extraction experiments. The results show that the CFCN achieved recall of 98.67%, 98.62%, and 99.52% and intersection over union (IoU) of 96.23%, 96.43%, and 95.76% in suburban, urban, and urban village areas, respectively. Considering the IoU in conjunction with the CFCN and U-net resulted in improvements of 6.61%, 5.31%, and 6.45% in suburban, urban, and urban village areas, respectively. The proposed method can extract buildings with higher accuracy and with clearer and more complete boundaries. |
| WOS关键词 | RESOLUTION SATELLITE IMAGERY ; REMOTE-SENSING IMAGERY ; LIDAR DATA ; DATA FUSION ; SEMANTIC SEGMENTATION ; OBJECT DETECTION ; AERIAL IMAGES ; SAR IMAGERY ; CLASSIFICATION |
| 资助项目 | National Natural Science Foundation of China[41590845] ; National Natural Science Foundation of China[41601405] ; National Natural Science Foundation of China[41525004] ; National Natural Science Foundation of China[41421001] ; Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) ; State Key Laboratory of Resources and Environmental Information System ; Fund of Jiangsu Provincial Land and Resources Science and Technology Project[2018054] ; Fund of Xuzhou Land and Resources Bureau Science and Technology Project[XZGTKJ2018001] ; Fund of Xuzhou Science and Technology Key R & D Program (Social Development) Project[KC18139] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000507333400028 |
| 资助机构 | National Natural Science Foundation of China ; Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) ; State Key Laboratory of Resources and Environmental Information System ; Fund of Jiangsu Provincial Land and Resources Science and Technology Project ; Fund of Xuzhou Land and Resources Bureau Science and Technology Project ; Fund of Xuzhou Science and Technology Key R & D Program (Social Development) Project |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/131458]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Wei |
| 作者单位 | 1.Zhejiang Design Inst Water Conservancy & Hydro El, Hangzhou 310002, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources, Beijing 100101, Peoples R China 3.Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Wei,Yang, MengYuan,Xie, Meng,et al. Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network[J]. REMOTE SENSING,2019,11(24):18. |
| APA | Liu, Wei.,Yang, MengYuan.,Xie, Meng.,Guo, Zihui.,Li, ErZhu.,...&Wang, Dong.(2019).Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network.REMOTE SENSING,11(24),18. |
| MLA | Liu, Wei,et al."Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network".REMOTE SENSING 11.24(2019):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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