长江科学院院报 ›› 2014, Vol. 31 ›› Issue (12): 107-112.DOI: 10.3969/j.issn.1001-5485.2014.12.022

• 信息技术应用 • 上一篇    下一篇

一种适用于云计算可扩展高分辨率遥感影像存储组织结构

沈盛彧1,刘哲2,张平仓1,张彤3,吴华意3,陈小平1   

  1. 1.长江科学院 水土保持研究所,武汉 430010;
    2.长江水利委员会 网络与信息中心,武汉 430010;
    3.武汉大学 测绘遥感信息工程国家重点实验室,武汉 430079
  • 收稿日期:2013-11-26 修回日期:2014-12-05 出版日期:2014-12-01 发布日期:2014-12-05
  • 作者简介:沈盛彧(1984- ),男,湖北武汉人,工程师,博士,主要从事高分辨遥感影像处理与水土保持研究,(电话)027-82926365(电子信箱)shenshenyu@mail.crsri.cn。
  • 基金资助:
    国家自然科学基金项目(41271400);中央级公益性科研院所基本科研业务费(CKSF2014024/TB,CKSF2012044/TB,CKSF2014055/TB)

A Scalable Structure for the Storage of High-resolution Remote
Sensing Images in Cloud Computing Environment

SHEN Sheng-yu1,LIU Zhe2,ZHANG Ping-cang1,ZHANG Tong3,WU Hua-yi3,CHEN Xiao-ping1   

  1. 1.Department of Soil and Water Conservation, Yangtze River Scientific Research Institute, Wuhan430010, China;
    2.Network and Information Center, Changjiang Water Resources Commission, Wuhan430010, China;
    3.State Key Laboratory of Information Engineering in Surveying, Mapping andRemote Sensing, Wuhan University, Wuhan 430079, China)
  • Received:2013-11-26 Revised:2014-12-05 Online:2014-12-01 Published:2014-12-05

摘要: 传统的遥感影像处理方法已无法有效应对当前遥感影像的3个“海量”问题,即日产量海量、单幅像素海量和可观测地物的类别及数据海量,使得多源海量遥感数据的利用率极其低下。为解决海量高分辨率遥感影像存储问题,提出了一种适用于云计算的高分辨率遥感影像存储组织结构,并对基于MapReduce框架的构建方法进行了详细的介绍。通过在Hadoop集群上对海量高分辨率遥感影像集进行的小影像集大文件构建方法实验与传统同类方式读取效率的对比,证明了本存储组织结构具有较高的扩展性,该小影像集大文件构建方法具有高效和高扩展的数据读写和处理能力,适合于作为处理海量高分辨率遥感影像的数据源。

关键词: 云计算, 高分辨率遥感影像, 存储组织结构, MapReduce, 小影像集大文件, Hadoop

Abstract: Traditional methods of processing remote sensing images could not effectively handle the mass daily production, mass pixel of single image, as well as the mass type and amount of objects. To solve the problem of image storage, we propose a structure for the storage of high-resolution remote sensing images in cloud computing environment, and expound the construction method based on MapReduce framework. We conducted experiments on large files of small image set in a Hadoop cluster and compared the image reading efficiency with that of traditional methods. The results proved that this storage structure has high scalability. Experiments also demonstrate this construction method has efficient reading/writing and processing ability.

Key words: cloud computing, high-resolution remote sensing image, storage structure, MapReduce, large files of small image sets, Hadoop

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