干旱区生态环境知识资源中心(Arid): Mapping of land cover in semi-arid regions based on a multi-gate semi-supervised learning method

Arid

DOI	10.1080/2150704X.2021.2012292
	Mapping of land cover in semi-arid regions based on a multi-gate semi-supervised learning method - a case study of Zhangbei, China
	Chen, Boan; Feng, Quanlong; Niu, Bowen; Yang, Jianyu; Gao, Bingbo; Liu, Jiantao; Wang, Weitao; Li, Chenxi; Zhao, Yuanyuan; Guo, Hao; Ma, Qin
通讯作者	Feng, QL (corresponding author), China Agr Univ, Coll Land Sci & Technol, 2 Yuanmingyuan Xilu, Beijing 100193, Peoples R China.
来源期刊	REMOTE SENSING LETTERS
ISSN	2150-704X
EISSN	2150-7058
出版年	2022
卷号	13 期号:2 页码:207-217
英文摘要	Semi-arid regions belong to important ecological transitional zones with a high ecosystem vulnerability and sensitivity. In recent years, due to the influence of climate change and human activities, the land covers of semi-arid regions have experienced dramatic changes. Therefore, timely and accurate land cover mapping of semi-arid regions is of great significance. Deep learning has been a hotspot in land cover mapping; however, it requires a large number of labelled samples. To tackle this data-hunger issue, we resolve to a multi-gate semi-supervised learning (SSL) method that could use a huge number of unlabelled samples to improve the classification performance. Specifically, the proposed method consists of a probability gate, an uncertainty gate and a consistency gate, aiming to generate high-quality pseudo-labels from the unlabelled datasets. Experimental results from Zhangbei, China indicate that the proposed method yields a good performance with an overall accuracy of 90.31%, which is 4.31% higher than that of traditional supervised learning (SL) methods. Ablation studies also verify the effectiveness of the gated mechanism in selecting unlabelled data.
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000731565100001
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	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/2XILL650/374502
作者单位	[Chen, Boan; Feng, Quanlong; Niu, Bowen; Yang, Jianyu; Gao, Bingbo; Li, Chenxi; Zhao, Yuanyuan; Guo, Hao] China Agr Univ, Coll Land Sci & Technol, 2 Yuanmingyuan Xilu, Beijing 100193, Peoples R China; [Liu, Jiantao] Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan, Peoples R China; [Wang, Weitao; Ma, Qin] China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Chen, Boan,Feng, Quanlong,Niu, Bowen,et al. Mapping of land cover in semi-arid regions based on a multi-gate semi-supervised learning method - a case study of Zhangbei, China[J],2022,13(2):207-217.
APA	Chen, Boan.,Feng, Quanlong.,Niu, Bowen.,Yang, Jianyu.,Gao, Bingbo.,...&Ma, Qin.(2022).Mapping of land cover in semi-arid regions based on a multi-gate semi-supervised learning method - a case study of Zhangbei, China.REMOTE SENSING LETTERS,13(2),207-217.
MLA	Chen, Boan,et al."Mapping of land cover in semi-arid regions based on a multi-gate semi-supervised learning method - a case study of Zhangbei, China".REMOTE SENSING LETTERS 13.2(2022):207-217.