地球信息科学学报

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (5): 681-689.doi: 10.3724/SP.J.1047.2016.00681

所属专题： 地理大数据

• 遥感大数据协同计算方法 • 上一篇    下一篇

大数据架构的遥感资源存储管理方法

胡晓东1(), 张新1, 屈靖生2   

  1. 1. 中国科学院遥感与数字地球研究所 遥感科学国家重点实验室,北京 100101
    2. 中科软科技股份有限公司,北京 100190
  • 收稿日期:2015-12-15 修回日期:2016-01-11 出版日期:2016-05-10 发布日期:2016-05-10
  • 作者简介:

    作者简介：胡晓东（1982-）,男,浙江绍兴人,博士,助理研究员,研究方向为遥感信息自适应计算、遥感大数据管理。E-mail:huxd@radi.ac.cn

  • 基金资助:
    国家自然科学青年基金项目(41301438);中国科学院重点部署项目（KZZD-EW-07-01）;中国科学院遥感与数字地球研究所“135”计划项目（Y3SG1500CX）

Resource Storage and Management Method of Massive Remote Sensing Data Supported by the Big Data Architecture

HU Xiaodong1,*(), ZHANG Xin1, QU Jingsheng2   

  1. 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth (CAS), Beijing 100101, China
    2. China soft Company Limited, Beijing 100190, China;
  • Received:2015-12-15 Revised:2016-01-11 Online:2016-05-10 Published:2016-05-10
  • Contact: HU Xiaodong E-mail:huxd@radi.ac.cn

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摘要：

随着遥感数据获取能力的日益增强,一方面导致遥感数据的多元化和海量化,使“存不起”的问题日益突出,另一方面由于缺少有效和高效的存储管理方法,难以及时发现终端应用所需的数据,使结果“存而无用”。本文围绕巨量、高吞吐、空间结构化的遥感影像数据及其基础土地信息产品的存储与管理问题,提出采用大数据架构的遥感资源存储管理方法,并基于MongoDB数据库实现了原型系统;通过使用PB量级数据进行试验,证明了该方法满足大数据时代对遥感矢栅数据的存储管理需求。

关键词: 遥感数据管理, 遥感大数据, 存储架构, 数据组织方式, MongoDB

Abstract:

The ability to acquire the remote sensing data is increasing day by day, which directly causes the remote sensing data to become diverse and massive, and the issue that the massive amount of data is being non-affordable to store has become more and more prominent. On the other hand, due to the lack of an effective and efficient method of storage management, the data that theterminal application need is difficult to found in a timely manner, therefore, is stored but useless. This paper focuses on the storage and management problems of the massive, high through put and spatially structured remote sensing data and the basic land information products. We have presented a storage and management method which uses the big data structure and can integrate both the vector and raster data. Based on the MongoDB database, the prototype system is realized and we use the data of PB rangeto test it. Eventually, we have proved that this method meets the demand for the storage and management of the remote sensing vector-raster data in the era of big data. On the basis of the study results and prototype system, the following studies need to be further explored: (1) The organization and management methods for internal data of resources, especially the objective and timely management for the vector data; (2) Real-time interactive visualization methods for different data types and storage modes of resources, achieving dynamic extraction and rendering ability based on in the heterogeneous data model; (3) To construct large data computing architecture on the heterogeneous type storage mode, and to implement multimodal computing framework to meet the needs of the remote sensing applications require.

Key words: remote sensing data management, remote sensing big data, storage architecture, data organizing mode, MongoDB