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

文献类型：期刊论文

作者Fang, Weizhen4; Sun, Yiyuan3; Ji, Rui2; Wan, Wei2; Ma, Lei1
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2021
卷号14页码:6363-6371
关键词Dams Image recognition Remote sensing Earth Training Internet Object recognition Convolutional neural networks (CNNs) dams deep learning Google earth object recognition
ISSN号1939-1404
DOI10.1109/JSTARS.2021.3088520
英文摘要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.
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
语种英语
WOS记录号WOS:000670543400005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17504]  
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.China Acad Elect & Informat Technol, Beijing 100041, Peoples R China
2.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China
3.Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, 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.

入库方式： OAI收割

来源：计算技术研究所

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