干旱区生态环境知识资源中心(Arid): Automated Characterization of Yardangs Using Deep Convolutional Neural Networks

Arid

DOI	10.3390/rs13040733
	Automated Characterization of Yardangs Using Deep Convolutional Neural Networks
	Gao, Bowen; Chen, Ninghua; Blaschke, Thomas; Wu, Chase Q.; Chen, Jianyu; Xu, Yaochen; Yang, Xiaoping; Du, Zhenhong
通讯作者	Chen, NH (corresponding author), Zhejiang Univ, Sch Earth Sci, Key Lab Geosci Big Data & Deep Resource Zhejiang, Hangzhou 310027, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:4
英文摘要	The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional distribution and evolution process. However, the existing automated methods to characterize yardangs are of limited generalization that may only be feasible for specific types of yardangs in certain areas. Deep learning methods, which are superior in representation learning, provide potential solutions for mapping yardangs with complex and variable features. In this study, we apply Mask region-based convolutional neural networks (Mask R-CNN) to automatically delineate and classify yardangs using very high spatial resolution images from Google Earth. The yardang field in the Qaidam Basin, northwestern China is selected to conduct the experiments and the method yields mean average precisions of 0.869 and 0.671 for intersection of union (IoU) thresholds of 0.5 and 0.75, respectively. The manual validation results on images of additional study sites show an overall detection accuracy of 74%, while more than 90% of the detected yardangs can be correctly classified and delineated. We then conclude that Mask R-CNN is a robust model to characterize multi-scale yardangs of various types and allows for the research of the morphological and evolutionary aspects of aeolian landform.
英文关键词	aeolian landform yardang morphological characteristic deep learning Mask R-CNN Google Earth imagery
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000624420100001
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351483
作者单位	[Gao, Bowen; Chen, Ninghua; Xu, Yaochen; Yang, Xiaoping; Du, Zhenhong] Zhejiang Univ, Sch Earth Sci, Key Lab Geosci Big Data & Deep Resource Zhejiang, Hangzhou 310027, Peoples R China; [Blaschke, Thomas] Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria; [Wu, Chase Q.] New Jersey Inst Technol, Dept Comp Sci, Newark, NJ 07102 USA; [Chen, Jianyu] Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China
推荐引用方式 GB/T 7714	Gao, Bowen,Chen, Ninghua,Blaschke, Thomas,et al. Automated Characterization of Yardangs Using Deep Convolutional Neural Networks[J],2021,13(4).
APA	Gao, Bowen.,Chen, Ninghua.,Blaschke, Thomas.,Wu, Chase Q..,Chen, Jianyu.,...&Du, Zhenhong.(2021).Automated Characterization of Yardangs Using Deep Convolutional Neural Networks.REMOTE SENSING,13(4).
MLA	Gao, Bowen,et al."Automated Characterization of Yardangs Using Deep Convolutional Neural Networks".REMOTE SENSING 13.4(2021).