The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications

doi:10.3390/rs13152909

	The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications
	Zhao, Chuanpeng 1,2,3; Qin, Cheng-Zhi 2,3,4
刊名	REMOTE SENSING
	2021-08-01
卷号	13 期号:15 页码:23
关键词	classification scheme false positive misclassification non-mangrove vegetation near water mangrove map Google Earth Engine
DOI	10.3390/rs13152909
通讯作者	Qin, Cheng-Zhi(qincz@lreis.ac.cn)
英文摘要	Accurate large-area mangrove classification is a challenging task due to the complexity of mangroves, such as abundant species within the mangrove category, and various appearances resulting from a large latitudinal span and varied habitats. Existing studies have improved mangrove classifications by introducing time series images, constructing new indices sensitive to mangroves, and correcting classifications by empirical constraints and visual inspections. However, false positive misclassifications are still prevalent in current classification results before corrections, and the key reason for false positive misclassification in large-area mangrove classifications is unknown. To address this knowledge gap, a hypothesis that an inadequate classification scheme (i.e., the choice of categories) is the key reason for such false positive misclassification is proposed in this paper. To validate this hypothesis, new categories considering non-mangrove vegetation near water (i.e., within one pixel from water bodies) were introduced, which is inclined to be misclassified as mangroves, into a normally-used standard classification scheme, so as to form a new scheme. In controlled conditions, two experiments were conducted. The first experiment using the same total features to derive direct mangrove classification results in China for the year 2018 on the Google Earth Engine with the standard scheme and the new scheme respectively. The second experiment used the optimal features to balance the probability of a selected feature to be effective for the scheme. A comparison shows that the inclusion of the new categories reduced the false positive pixels with a rate of 71.3% in the first experiment, and a rate of 66.3% in the second experiment. Local characteristics of false positive pixels within 1 x 1 km cells, and direct classification results in two selected subset areas were also analyzed for quantitative and qualitative validation. All the validation results from the two experiments support the finding that the hypothesis is true. The validated hypothesis can be easily applied to other studies to alleviate the prevalence of false positive misclassifications.
资助项目	Science and Technology Basic Resources Investigation Program of China[2017FY100706] ; Chinese Academy of Sciences[XDA23100503]
WOS关键词	FOREST ; CHINA ; MARINE ; CONSERVATION ; VARIABILITY ; ECOSYSTEMS ; ALGORITHMS ; REGRESSION ; IMAGERY ; INDEX
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000682191900001
资助机构	Science and Technology Basic Resources Investigation Program of China ; Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/164685]
专题	中国科学院地理科学与资源研究所
通讯作者	Qin, Cheng-Zhi
作者单位	1.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Changchun 130102, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Chuanpeng,Qin, Cheng-Zhi. The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications[J]. REMOTE SENSING,2021,13(15):23.
APA	Zhao, Chuanpeng,&Qin, Cheng-Zhi.(2021).The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications.REMOTE SENSING,13(15),23.
MLA	Zhao, Chuanpeng,et al."The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications".REMOTE SENSING 13.15(2021):23.