气候变化领域集成服务门户(CCPortal): Automatic training sample collection utilizing multi-source land cover products and time-series Sentinel-2 images

CCPortal

DOI	10.1080/15481603.2024.2352957
	Automatic training sample collection utilizing multi-source land cover products and time-series Sentinel-2 images
	Wang, Yanzhao; Sun, Yonghua; Cao, Xuyue; Wang, Yihan; Zhang, Wangkuan; Cheng, Xinglu; Wang, Ruozeng; Zong, Jinkun
发表日期	2024
ISSN	1548-1603
EISSN	1943-7226
起始页码	61
结束页码	1
卷号	61 期号:1
英文摘要	Collecting reliable training samples plays a crucial role in improving the accuracy of land cover (LC) mapping products, which are essential foundational data for global environmental and climate change research. However, the process is labor-intensive and time-consuming, as it heavily relies on human interpretation. This article proposes an automatic training sample collection approach (ATSC) that utilizes multi-source LC products and time-series Sentinel-2 images. Firstly, a preliminary sample dataset was generated by fusing multiple LC products with the weighted majority voting (WMV) algorithm. Secondly, a locally selective combination in parallel outlier ensembles (LSCP) anomaly detection algorithm was applied to filter abnormal samples. The results revealed that (1) the China Land Cover Dataset (CLCD) had the highest overall accuracy (73.22%), and the ESRI Land Cover (ESRI) had the lowest overall accuracy (59.93%). Tree cover, built area, and water showed high accuracy across all products, while shrubland and wetland generally had low accuracy. (2) The average accuracy of the preliminary training samples for the four study areas was 95.62%. However, there were still abnormal samples, such as classification errors, LC changes within a year, and spectral anomalies. (3) Using the LSCP algorithm, 70.10% of the abnormal samples were removed, resulting in a final training sample accuracy that exceeded 97.95% in each region. The ATSC approach provides higher-quality training samples for LC classification and facilitates large-scale LC mapping initiatives.
英文关键词	Land cover; training samples; automatic sample collection; weighted majority voting; anomaly detection
语种	英语
WOS研究方向	Physical Geography ; Remote Sensing
WOS类目	Geography, Physical ; Remote Sensing
WOS记录号	WOS:001222917500001
来源期刊	GISCIENCE & REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298510
作者单位	Capital Normal University; Capital Normal University; Capital Normal University
推荐引用方式 GB/T 7714	Wang, Yanzhao,Sun, Yonghua,Cao, Xuyue,et al. Automatic training sample collection utilizing multi-source land cover products and time-series Sentinel-2 images[J],2024,61(1).
APA	Wang, Yanzhao.,Sun, Yonghua.,Cao, Xuyue.,Wang, Yihan.,Zhang, Wangkuan.,...&Zong, Jinkun.(2024).Automatic training sample collection utilizing multi-source land cover products and time-series Sentinel-2 images.GISCIENCE & REMOTE SENSING,61(1).
MLA	Wang, Yanzhao,et al."Automatic training sample collection utilizing multi-source land cover products and time-series Sentinel-2 images".GISCIENCE & REMOTE SENSING 61.1(2024).