Recognizing Global Dams From High-Resolution Remotely Sensed Images Using Convolutional Neural Networks

doi:10.1109/JSTARS.2021.3088520

	Recognizing Global Dams From High-Resolution Remotely Sensed Images Using Convolutional Neural Networks
	Fang, Weizhen 1; Sun, Yiyuan 2; Ji, Rui 3; Wan, Wei 3; Ma, Lei 4
	2021
发表期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
卷号	14 页码:6363-6371
摘要	Dams constructed by humans are important facilities for irrigation, flood control, and power generation. Recognizing the location and number of dams is crucial for studying the impact of human activities on ecosystem change. Although many countries and organizations have established their own dam datasets, it is only the tip of the iceberg of real dam construction. Therefore, effectively and accurately obtaining the geographic location of dams is still a significant problem to be solved. This article proposes an improved convolutional neural network (CNN) based framework to recognize global dams from high-resolution remotely sensed images. First, a dataset named the global dam detection dataset is built based on Google earth high-resolution images, and the dataset is used as the training and testing dataset for the CNN model. Second, an improved dam recognition method (HRLibra-RCNN) is proposed to detect dams on a global scale. Third, an application strategy for global dam recognition from large remote sensing images is established to recognize dams in seven regions around the world. Compared with two two-stage object recognition models (Faster-RCNN and Cascade-RCNN) and a single-stage target detection model (RetinaNet), the proposed method achieved the highest average precision of 79.4%, with the HRNet-40w backbone network structures achieving the highest average precision of 80.7%. The average precision of 70.8% and recall of 90.4% are achieved during the application stage. The dataset and framework developed in this study are the first attempts to combine remote sensing big data and the deep learning method to recognize dams at a global scale.
关键词	Dams Image recognition Remote sensing Earth Training Internet Object recognition Convolutional neural networks (CNNs) dams deep learning Google earth object recognition
DOI	10.1109/JSTARS.2021.3088520
收录类别	SCI
语种	英语
资助项目	Chinese Defence Innovation Project of Science and Technology ; National Natural Science Foundation of China[41971377]
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000670543400005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17505
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wan, Wei; Ma, Lei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China 3.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China 4.China Acad Elect & Informat Technol, Beijing 100041, Peoples R China
推荐引用方式 GB/T 7714	Fang, Weizhen,Sun, Yiyuan,Ji, Rui,et al. Recognizing Global Dams From High-Resolution Remotely Sensed Images Using Convolutional Neural Networks[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:6363-6371.
APA	Fang, Weizhen,Sun, Yiyuan,Ji, Rui,Wan, Wei,&Ma, Lei.(2021).Recognizing Global Dams From High-Resolution Remotely Sensed Images Using Convolutional Neural Networks.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,6363-6371.
MLA	Fang, Weizhen,et al."Recognizing Global Dams From High-Resolution Remotely Sensed Images Using Convolutional Neural Networks".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):6363-6371.