气候变化领域集成服务门户(CCPortal): A dynamic classification scheme for mapping spectrally similar classes: Application to wetland classification

CCPortal

DOI	10.1016/j.jag.2019.101914
	A dynamic classification scheme for mapping spectrally similar classes: Application to wetland classification
	Mahdavi S.; Salehi B.; Amani M.; Granger J.; Brisco B.; Huang W.
发表日期	2019
ISSN	15698432
卷号	83
英文摘要	Applying numerous features is common for complex land cover classification, which makes feature selection necessary. Although the selected feature subsets may yield high overall accuracies in the case of spectrally similar classes such as wetlands, several individual accuracies are often low. A reason is that one set of features used to separate one specific class from the rest might not be appropriate for delineating another class. An additional reason is that while the overall accuracy is applied to evaluate the potential of a feature subset, it may be influenced by a few high-accuracy classes that are spectrally distinct and for which collecting enough training data is feasible. In this article, rather than simultaneously mapping all classes, they were individually classified using a different feature selection. Spectral analysis was applied to determine both the order of the classes to be mapped and a merging scheme that was applied to the remaining classes to increase the accuracy of the target class. The proposed approach was applied to wetland mapping using five pilot sites throughout Newfoundland and Labrador, Canada. The dataset available for each pilot site differed in quality and quantity. However, the proposed method accurately classified wetlands in all pilot sites, even those with limited satellite and/or field data, and outperformed the classic method by increasing the average producer and user accuracies of wetlands by up to 22% and 25%, respectively, and yielding overall accuracies up to 93%. Among wetland classes, Shallow Water was easier to be distinguished, as a result of being spectrally less similar to the rest of the wetland classes. Based on the obtained results, the proposed method can be effectively applied for classifications involving spectrally similar classes, including sea ice and crop mapping. © 2019 Elsevier B.V.
英文关键词	Classification; Feature selection; Remote sensing; Wetlands
语种	英语
scopus关键词	accuracy assessment; land classification; land cover; mapping method; remote sensing; Canada; Newfoundland and Labrador
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156437
作者单位	Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St John's, NL A1B 3X7, Canada; Wood Environment and Infrastructure Solutions, a Division of Wood Canada Limited, St. John's, NL A1B 1H3, Canada; C-CORE, St. John's, NL A1B 3X5, Canada; College of Environmental Science and Forestry (ESF), State University of New York (SUNY), United States; The Canada Center for Mapping and Earth Observation, Ottawa, ON K1S 5K2, Canada
推荐引用方式 GB/T 7714	Mahdavi S.,Salehi B.,Amani M.,et al. A dynamic classification scheme for mapping spectrally similar classes: Application to wetland classification[J],2019,83.
APA	Mahdavi S.,Salehi B.,Amani M.,Granger J.,Brisco B.,&Huang W..(2019).A dynamic classification scheme for mapping spectrally similar classes: Application to wetland classification.International Journal of Applied Earth Observation and Geoinformation,83.
MLA	Mahdavi S.,et al."A dynamic classification scheme for mapping spectrally similar classes: Application to wetland classification".International Journal of Applied Earth Observation and Geoinformation 83(2019).