气候变化领域集成服务门户(CCPortal): Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping

CCPortal

DOI	10.1016/j.rse.2019.111563
	Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping
	Li W.; Dong R.; Fu H.; Wang J.; Yu L.; Gong P.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	Land use and land cover maps provide fundamental information that has been used in different kinds of studies, ranging from climate change to city planning. However, despite substantial efforts in recent decades, large-scale 30-m land cover maps still suffer from relatively low accuracy in terms of land cover type discrimination (especially for the vegetation and impervious types), due to limits in relation to the data, method, and design of the workflow. In this work, we improved the land cover classification accuracy by integrating free and public high-resolution Google Earth images (HR-GEI) with Landsat Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+) imagery. Our major innovation is a hybrid approach that includes three major components: (1) a deep convolutional neural network (CNN)-based classifier that extracts high-resolution features from Google Earth imagery; (2) traditional machine learning classifiers (i.e., Random Forest (RF) and Support Vector Machine (SVM)) that are based on spectral features extracted from 30-m Landsat data; and (3) an ensemble decision maker that takes all different features into account. Experimental results show that our proposed method achieves a classification accuracy of 84.40% on the entire validation dataset in China, improving the previous state-of-the-art accuracies obtained by RF and SVM by 4.50% and 4.20%, respectively. Moreover, our proposed method reduces misclassifications between certain vegetation types, and improves identification of the impervious type. Evaluation applied over an area of around 14,000 km2 confirms little improvement for land cover types (e.g., forest) of which the classification accuracies are already over 80% when using traditional machine learning approaches, yet improvements in accuracy of 7% for cropland and shrubland, 9% for grassland, 23% for impervious and 25% for wetlands were achieved when compared with traditional machine learning approaches. The results demonstrate the great potential of integrating features of datasets at different resolutions and the possibility to produce more reliable land cover maps. © 2019
英文关键词	30-m land cover mapping; Data fusion; Deep learning; High-resolution Google Earth imagery; Landsat
语种	英语
scopus关键词	Classification (of information); Climate change; Data fusion; Decision making; Decision trees; Deep learning; Deep neural networks; Land use; Machine components; Machine learning; Mapping; Neural networks; Support vector machines; Vegetation; Classification accuracy; Convolutional neural network; Enhanced thematic mapper plus (ETM+); Google earths; Land cover classification; Land cover mapping; LANDSAT; Machine learning approaches; Image enhancement; accuracy assessment; algorithm; artificial nest; artificial neural network; climate change; data acquisition; integrated approach; land cover; land use; Landsat; machine learning; mapping method; satellite data; satellite imagery; support vector machine; vegetation type; China
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179531
作者单位	Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China; Joint Center for Global Change Studies (JCGCS), Beijing, 100084, China; CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong, Hong Kong; State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China
推荐引用方式 GB/T 7714	Li W.,Dong R.,Fu H.,et al. Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping[J],2020,237.
APA	Li W.,Dong R.,Fu H.,Wang J.,Yu L.,&Gong P..(2020).Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping.Remote Sensing of Environment,237.
MLA	Li W.,et al."Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping".Remote Sensing of Environment 237(2020).