Geographic Optimal Transport for Heterogeneous Data: Fusing Remote Sensing and Social Media

doi:10.1109/TGRS.2020.3031337

	Geographic Optimal Transport for Heterogeneous Data: Fusing Remote Sensing and Social Media
	Liu, Zhenjie 1; Qiu, Qiang 2; Li, Jun 1; Wang, Lizhe 3; Plaza, Antonio 4
	2021-08-01
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	59 期号:8 页码:6935-6945
摘要	The fusion of heterogeneous remote sensing and social media data can fill the gaps in satellite image collections and improve the spatiotemporal resolution of the available data sets. As a result, it is being gradually adopted in multimodal data analytics. Generally, the fusion of heterogeneous geographic data faces the following issues: 1) the probability density functions may differ from different data sources and 2) the geolocations may not be well aligned. The former one can be generally solved by performing an alignment of representations in the source and target domains using, for instance, domain adaptation. The latter issue is seldom considered in the fusion of heterogeneous geographic data. In this article, we present a new method called geographic optimal transport (GOT), which aims at aligning representations and geolocations in a simultaneous fashion. A flood event that took place in 2013 in Boulder, CO, USA, is taken as a case study to evaluate our GOT method. Here, we consider two remote sensing features derived from water indicators, i.e., the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), for the fusion of Landsat 8 imagery and Twitter data. A comparison between our newly developed GOT and the traditional optimal transport (OT) is performed. Experimental results demonstrate that the proposed GOT can accurately align spatially biased georeferenced tweets to the flood phenomena, leading to the conclusion that GOT can effectively fuse heterogeneous remote sensing and social media data.
关键词	Remote sensing Geology Social networking (online) Earth Artificial satellites Satellites Indexes Data fusion geolocation alignment remote sensing representation alignment social media
DOI	10.1109/TGRS.2020.3031337
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19090104]
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000675402300055
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17306
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Jun
作者单位	1.Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China 4.Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10071, Spain
推荐引用方式 GB/T 7714	Liu, Zhenjie,Qiu, Qiang,Li, Jun,et al. Geographic Optimal Transport for Heterogeneous Data: Fusing Remote Sensing and Social Media[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021,59(8):6935-6945.
APA	Liu, Zhenjie,Qiu, Qiang,Li, Jun,Wang, Lizhe,&Plaza, Antonio.(2021).Geographic Optimal Transport for Heterogeneous Data: Fusing Remote Sensing and Social Media.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(8),6935-6945.
MLA	Liu, Zhenjie,et al."Geographic Optimal Transport for Heterogeneous Data: Fusing Remote Sensing and Social Media".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.8(2021):6935-6945.