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An Improved Deep Learning Approach for Retrieving Outfalls Into Rivers From UAS Imagery

文献类型：期刊论文

作者Huang, Yaohuan2,3; Wu, Chengbin2,3; Yang, Haijun1; Zhu, Haitao1; Chen, Mingxing2,3; Yang, Jie2,3
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2021-09-30
页码14
ISSN号0196-2892
关键词Rivers Visualization Remote sensing Manuals Inspection Water resources Task analysis Deep learning digital surface model (DSM) faster region convolutional neural network (R-CNN) outfalls into river unmanned aircraft systems (UAS) imagery
DOI10.1109/TGRS.2021.3113901
通讯作者Wu, Chengbin(wucb.19s@igsnrr.ac.cn)
英文摘要Outfalls into rivers are the final gate of anthropogenic pollution flowing to receiving waters, which means that outfall surveys are significant to basin environmental protection and ecosystem health management. Unmanned aircraft systems (UAS) with high spatial resolution imagery have become important data for ongoing surveys of outfalls. However, outfalls retrieval from UAS imagery is inefficient to visual interpretation and a challenging task for traditional spectral-based and object-oriented classification methods given the problems of salt-and-pepper noise and scale selection. In this study, an improved geo-deep learning approach based on the faster region convolutional neural network (R-CNN) architecture (GDCNN-outfalls) is proposed for retrieving outfalls into rivers with UAS imagery. In the proposed method, three tactics--anchor size, region of interest (RoI), and hard negative mining--were adopted to optimize the benchmark Faster R-CNN application in outfalls retrieval. Meanwhile, a geo-classifier module with digital surface model (DSM) enhancement and a spatial activation function was integrated with the Faster R-CNN architecture to generate GDCNN-outfalls. The validation experiments indicated that GDCNN-outfalls improved the performance of Faster R-CNN in outfall retrieval by suppressing false positive (FPs) from 33.52% to 26.14% and increasing the F1 score from 0.72 to 0.75. The test results confirm the performance of GDCNN-outfalls with a recall of 79.3% and higher precision (48.4%) than that of Faster R-CNN (2.1%), also show the GDCNN-outfalls is ten times faster than visual interpretation. This study demonstrates that the combination of deep learning and UAS techniques can be a feasible solution to detect outfalls in outfall surveys.
WOS关键词OBJECT DETECTION ; NEURAL-NETWORK ; WATER-QUALITY ; EXAMPLE ; PLUMES ; IMPACT
资助项目National Natural Science Foundation of China[41822104] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040402] ; National Key Research and Development Program of China[2017YFB0503005] ; National Science and Technology Major Project of China's High Resolution Earth Observation system[21-Y20B01-900119/22]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000732766000001
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; National Science and Technology Major Project of China's High Resolution Earth Observation system
源URL[http://ir.igsnrr.ac.cn/handle/311030/168696]  
专题中国科学院地理科学与资源研究所
通讯作者Wu, Chengbin
作者单位1.Minist Ecol & Environm, Ctr Satellite Applicat Ecol & Environm, Beijing 100094, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resource & Environm, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Yaohuan,Wu, Chengbin,Yang, Haijun,et al. An Improved Deep Learning Approach for Retrieving Outfalls Into Rivers From UAS Imagery[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021:14.
APA Huang, Yaohuan,Wu, Chengbin,Yang, Haijun,Zhu, Haitao,Chen, Mingxing,&Yang, Jie.(2021).An Improved Deep Learning Approach for Retrieving Outfalls Into Rivers From UAS Imagery.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,14.
MLA Huang, Yaohuan,et al."An Improved Deep Learning Approach for Retrieving Outfalls Into Rivers From UAS Imagery".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021):14.

入库方式： OAI收割

来源：地理科学与资源研究所

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