globalchange  > 气候变化与战略
DOI: 10.1016/j.isprsjprs.2020.01.010
论文题名:
The migration of training samples towards dynamic global land cover mapping
作者: Huang H.; Wang J.; Liu C.; Liang L.; Li C.; Gong P.
刊名: ISPRS Journal of Photogrammetry and Remote Sensing
ISSN: 9242716
出版年: 2020
卷: 161
语种: 英语
英文关键词: Change detection ; Classification ; Cloud computing ; Training sample
Scopus关键词: Classification (of information) ; Climate change ; Cloud computing ; Mapping ; Pixels ; Change detection ; Classification accuracy ; Classification results ; Cloud based platforms ; Landsat-5 TM images ; Spectral distances ; Spectral similarity ; Training sample ; Sampling ; accuracy assessment ; image analysis ; land cover ; Landsat ; satellite imagery ; supervised classification ; training
英文摘要: High quality training samples are essential for global land cover mapping. Traditionally, training samples are collected by field work or via manual interpretation based on high-resolution Google Earth images. Due to the difficulty of training sample collection, regular global land cover mapping is still a challenge. In this study, we developed an automatic training sample migration method based on the first all-season sample set in 2015 and all available archived Landsat 5 TM images in the Google Earth Engine cloud-based platform. By measuring the spectral similarity and spectral distance between the reference spectral and image spectral, we detected and identified the change state of training sample pixels in 2010, 2005, 2000, 1995, and 1990. Overall, 170,925 (66%), 118,586 (64%), 112,092 (67%), 154,931 (63%), and 147,267 (60%) respective training sample pixels were found with no changes over each five-year period. The detection (user's) accuracies of migrated training sample pixels as no change for the first four time periods were 99.25%, 97.65%, 95.03%, and 92.98%, respectively, by comparing with CCI-LC (Climate Change Initiative Land Cover) maps. Classification experiment showed that the migrated training samples can obtain a similar classification accuracy of 71.42% in 2010, when compared to the classification result in 2015 using the same number of training samples. Our study provides a potential solution to resolve the problem of lack of training samples for dynamic global land cover mapping efforts. © 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158800
Appears in Collections:气候变化与战略

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作者单位: School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou, 510275, China; State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China; Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, United States; Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720-3114, United States

Recommended Citation:
Huang H.,Wang J.,Liu C.,et al. The migration of training samples towards dynamic global land cover mapping[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2020-01-01,161
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