Automatic Remote Sensing Detection of Floating Macroalgae in the Yellow and East China Seas Using Extreme Learning Machine

doi:10.2112/SI90-034.1

	Automatic Remote Sensing Detection of Floating Macroalgae in the Yellow and East China Seas Using Extreme Learning Machine
	Liang, Xi-Jian 1,2; Qin, Ping 2; Xiao, Yan-Fang 1; Kim, Keun-Yong 3; Liu, Rong-Jie 1; Chen, Xiao-Ying 1; Wang, Quan-Bin 1
刊名	JOURNAL OF COASTAL RESEARCH
	2019-06
页码	272-281
关键词	Automatic detection macroalgae extreme learning machine GF-1
ISSN号	0749-0208
DOI	10.2112/SI90-034.1
英文摘要	In the past 10 years, floating macroalgae blooms have occurred repeatedly in the Yellow Sea. For the purpose of disaster prevention and mitigation, it is very important to monitor floating macroalgae blooms using satellite imagery. The traditional macroalgae remote sensing detection methods based on the vegetation indices are very sensitive to the threshold value which is affected by many factors in the complex atmospheric-oceanic environment. The threshold has obvious temporal and spatial variations, and is difficult to determine accurately. The expert experience is required to assist the value of threshold which leads to the low automation of detection. Aiming at this problem, this study introduces an Extreme Learning Machine (ELM) into the field of macroalgae remote sensing detection. Taking the four-bands GF-1 WFV optical images with 16-m resolution as an example, an automatic remote sensing detection model of macroalgae is constructed. The evaluation based on independent data shows that the accuarcy of this method is up to 86 %. The method is not disturbed by thin clouds, sun glint, high-turbidity water, and other factors. In addition, no manual intervention is required which suggests that the proposed method has strong potential of automated detection for the floating macroalgae blooms.
资助项目	Dragon Programme[32405]
WOS关键词	BLOOMS ; ACCURACY
WOS研究方向	Environmental Sciences & Ecology ; Physical Geography ; Geology
语种	英语
出版者	COASTAL EDUCATION & RESEARCH FOUNDATION
WOS记录号	WOS:000485714500035
内容类型	期刊论文
源URL	[http://ir.fio.com.cn:8080/handle/2SI8HI0U/30230]
专题	自然资源部第一海洋研究所
通讯作者	Xiao, Yan-Fang
作者单位	1.Minist Nat Resources, Inst Oceanog 1, Lab Marine Phys & Remote Sensing, Qingdao, Shandong, Peoples R China 2.Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Shandong, Peoples R China 3.Korea Inst Ocean Sci & Technol, Korea Ocean Satellite Ctr, Busan, South Korea
推荐引用方式 GB/T 7714	Liang, Xi-Jian,Qin, Ping,Xiao, Yan-Fang,et al. Automatic Remote Sensing Detection of Floating Macroalgae in the Yellow and East China Seas Using Extreme Learning Machine[J]. JOURNAL OF COASTAL RESEARCH,2019:272-281.
APA	Liang, Xi-Jian.,Qin, Ping.,Xiao, Yan-Fang.,Kim, Keun-Yong.,Liu, Rong-Jie.,...&Wang, Quan-Bin.(2019).Automatic Remote Sensing Detection of Floating Macroalgae in the Yellow and East China Seas Using Extreme Learning Machine.JOURNAL OF COASTAL RESEARCH,272-281.
MLA	Liang, Xi-Jian,et al."Automatic Remote Sensing Detection of Floating Macroalgae in the Yellow and East China Seas Using Extreme Learning Machine".JOURNAL OF COASTAL RESEARCH (2019):272-281.