A Novel Spectral-Unmixing-Based Green Algae Area Estimation Method for GOCI Data

doi:10.1109/JSTARS.2016.2585161

	A Novel Spectral-Unmixing-Based Green Algae Area Estimation Method for GOCI Data
	Pan, Bin 1,2,3; Shi, Zhenwei 1,2,3; An, Zhenyu 1,2,3; Jiang, Zhiguo 2,3; Ma, Yi 4
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2017-02
卷号	10 期号:2 页码:437-449
关键词	Area estimation fast endmember extraction green algae blooms spectral unmixing
ISSN号	1939-1404
DOI	10.1109/JSTARS.2016.2585161
英文摘要	Geostationary OceanColor Imager (GOCI) data have been widely used in the detection and area estimation of green algae blooms. However, due to the low spatial resolution of GOCI data, pixels in an image are usually "mixed," which means that the region a pixel covers may include many different materials. Traditional index-based methods can detect whether there are green algal blooms in each pixel, whereas it is still challenging to determine the proportion that green algae blooms occupy in a pixel. In this paper, we propose a novel subpixel-level area estimation method for green algae blooms based on spectral unmixing, which can not only detect the existence of green algae but also determine their proportion in each pixel. A fast endmember extraction method is proposed to automatically calculate the endmember spectral matrix, and the abundance map of green algae which could be regarded as the area estimation is obtained by nonnegatively constrained least squares. This new fast endmembers extraction technique outperforms the classical N-FINDR method by applying two models: candidates location and distance-based vertices determination. In the first model, we propose a medium-distance-based candidates location strategy, which could reduce the searching space during vertices selection. In the second model, we replace the simplex volume measure with a more simple distance measure, thus complex matrix determinant calculation is avoided. We have theoretically proven the equivalency of volume and distance measure. Experiments on GOCI data and synthetic data demonstrate the superiority of the proposed method compared with some state-of-art approaches.
资助项目	Fundamental Research Funds for the Central Universities[YWF-14-YHXY-028] ; Fundamental Research Funds for the Central Universities[YWF-15-YHXY-003]
WOS关键词	REMOTELY-SENSED IMAGERY ; HYPERSPECTRAL DATA ; ENDMEMBER EXTRACTION ; COMPONENT ANALYSIS ; FAST ALGORITHM ; YELLOW SEA ; BLOOMS
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000395466700006
内容类型	期刊论文
源URL	[http://ir.fio.com.cn:8080/handle/2SI8HI0U/25547]
专题	自然资源部第一海洋研究所
通讯作者	Shi, Zhenwei
作者单位	1.Beihang Univ, Sch Astronaut, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China 2.Beihang Univ, Beijing Key Lab Digital Media, Beijing 100191, Peoples R China 3.Beihang Univ, Sch Astronaut, Image Proc Ctr, Beijing 100191, Peoples R China 4.State Ocean Adm, Inst Oceanog 1, Qingdao 266061, Peoples R China
推荐引用方式 GB/T 7714	Pan, Bin,Shi, Zhenwei,An, Zhenyu,et al. A Novel Spectral-Unmixing-Based Green Algae Area Estimation Method for GOCI Data[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2017,10(2):437-449.
APA	Pan, Bin,Shi, Zhenwei,An, Zhenyu,Jiang, Zhiguo,&Ma, Yi.(2017).A Novel Spectral-Unmixing-Based Green Algae Area Estimation Method for GOCI Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,10(2),437-449.
MLA	Pan, Bin,et al."A Novel Spectral-Unmixing-Based Green Algae Area Estimation Method for GOCI Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10.2(2017):437-449.