中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Geographic Optimal Transport for Heterogeneous Data: Fusing Remote Sensing and Social Media

文献类型：期刊论文

作者Liu, Zhenjie1; Qiu, Qiang2; Li, Jun1; Wang, Lizhe3; Plaza, Antonio4
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2021-08-01
卷号59期号:8页码:6935-6945
关键词Remote sensing Geology Social networking (online) Earth Artificial satellites Satellites Indexes Data fusion geolocation alignment remote sensing representation alignment social media
ISSN号0196-2892
DOI10.1109/TGRS.2020.3031337
英文摘要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.
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
语种英语
WOS记录号WOS:000675402300055
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17305]  
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。