| 基于GeoSOT区位标识的多源遥感数据组织研究 |
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| 引用本文： | 吕雪锋,廖永丰,程承旗,金安. 基于GeoSOT区位标识的多源遥感数据组织研究[J]. 北京大学学报(自然科学版), 2014, 50(2): 331 |
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| 作者姓名： | 吕雪锋 廖永丰 程承旗 金安 |
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| 作者单位： | 1. 民政部国家减灾中心, 北京 100124; 2. 民政部灾害评估与风险防范重点实验室, 北京 100124; 3. 北京大学工学院航空航天信息工程研究所, 北京 100871; 4. 北京大学遥感与地理信息系统研究所, 北京 100871; |
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| 基金项目： | 国家重点基础研究发展计划(61399);海洋公益性行业科研专项(201305020)资助 |
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| 摘 要： | 针对全球多源遥感数据的统一区位组织与多尺度提取问题, 在GeoSOT地球剖分格网框架下, 采用GeoSOT区位标识模型设计多源遥感数据的GeoSOT区位组织方法。该方法通过对多源遥感数据所覆盖的区域范围和位置进行统一结构化面片标识, 将遥感数据的外部整体定位和内部数据块结构都与地球空间区位形成一致性关联, 能够更高效地组织与管理全球范围内同属一个地理区域的多源遥感数据。
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| 关 键 词： | 区位标识 影像组织 地球剖分 多尺度提取 |
| 收稿时间： | 2013-02-17 |
Multi-Source Remote Sensing Data Organization Based on GeoSOT Location Identification |
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| Lü Xuefeng,LIAO Yongfeng,CHENG Chengqi,JIN An. Multi-Source Remote Sensing Data Organization Based on GeoSOT Location Identification[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2014, 50(2): 331 |
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| Authors: | Lü Xuefeng LIAO Yongfeng CHENG Chengqi JIN An |
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| Affiliation: | 1. National Disaster Reduction Center of China, Beijing 100124; 2. Key Laboratory of Integrated Disaster Assessment and Risk Governance of the Ministry of Civil Affairs, Beijing 100124; 3. Institute for Aeronautics and Astronautics Information Engineering, College of Engineering, Peking University, Beijing 100871; 4. Institute of Remote Sensing and Geographical Information System, Peking University, Beijing 100871; |
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| Abstract: | Aiming at unified geospatial location organization and multi-scale extraction problems of global multi-source remote sensing data, a kind of unified location organization method, which adopts the GeoSOT location identification model based on global subdivision grid, is designed. In this method, geospatial regional range and position covered by multi-source remote sensing data are uniformly identified with structured cells, and a high consistent correlation, which exists between the external outline positioning and internal data block structure of remote sensing data and geospatial location, is established. The principle and test analysis show that the proposed method can more efficiently organize and manage global multi-source remote sensing data that belong to a same geographic region. |
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| Keywords: | location identification image organization global subdivision grid multi-scale extraction |
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