分布式MODIS植被供水指数生产模型研究与实现

孟令奎, 朱会玲, 谢文君, 胡艺杰, 张文

孟令奎, 朱会玲, 谢文君, 胡艺杰, 张文. 分布式MODIS植被供水指数生产模型研究与实现[J]. 武汉大学学报 ( 信息科学版), 2018, 43(5): 676-683. DOI: 10.13203/j.whugis20150699
引用本文: 孟令奎, 朱会玲, 谢文君, 胡艺杰, 张文. 分布式MODIS植被供水指数生产模型研究与实现[J]. 武汉大学学报 ( 信息科学版), 2018, 43(5): 676-683. DOI: 10.13203/j.whugis20150699
MENG Lingkui, ZHU Huiling, XIE Wenjun, HU Yijie, ZHANG Wen. Distributed Production Method for MODIS VSWI Based on OSGI[J]. Geomatics and Information Science of Wuhan University, 2018, 43(5): 676-683. DOI: 10.13203/j.whugis20150699
Citation: MENG Lingkui, ZHU Huiling, XIE Wenjun, HU Yijie, ZHANG Wen. Distributed Production Method for MODIS VSWI Based on OSGI[J]. Geomatics and Information Science of Wuhan University, 2018, 43(5): 676-683. DOI: 10.13203/j.whugis20150699

分布式MODIS植被供水指数生产模型研究与实现

基金项目: 

国家重点研发计划 2017YFC0405806

高分辨率对地观测系统重大专项(民用部分) 08-Y30B07-9001-13/15

详细信息
    作者简介:

    孟令奎, 教授, 主要从事遥感和地理信息系统研究。lkmeng@whu.edu.cn

    通讯作者:

    张文, 博士, 讲师。wen_zhang@whu.edu.cn

  • 中图分类号: P237

Distributed Production Method for MODIS VSWI Based on OSGI

Funds: 

The National Key Research and Development Program of China 2017YFC0405806

The National High-Resolution Earth Observation System Projects (Civil Part) 08-Y30B07-9001-13/15

More Information
    Author Bio:

    MENG Lingkui, PhD, professor, specializes in remote sensing and geography information system. E-mail: lkmeng@whu.edu.cn

    Corresponding author:

    ZHANG Wen, PhD, lecturer. E-mail: wen_zhang@whu.edu.cn

  • 摘要: 遥感监测已成为旱情监测的重要手段,其中植被供水指数(vegetation supply water index,VSWI)产品是进行遥感旱情监测的重要参考依据,但其生产涉及的数据量大,处理周期长,严重影响监测数据处理的及时性。面向分布式计算环境,提出了一种基于开放服务网关协议(open services gateway initiative,OSGI)的分布式中分辨率成像光谱仪(Moderate-Resolution Imaging Spectroradiometer,MODIS)植被供水指数产品生产模型(OSGI-based VSWI distributed production model,ODPM),通过该模型将MODIS影像的植被供水指数产品生产算法转化成OSGI的Bundle组件,使其能够在分布式OSGI的平台中进行动态的部署与安装,充分利用局域网内的计算资源,实现快速的遥感数据处理与监测产品生产。利用中国7大流域数据对模型的稳定性进行实验,结果表明,相对传统的单机处理模式,该模型的运算速度提升3倍左右,内存占用减少大约2/3倍,并具有良好的计算稳定性。ODPM模型在提升数据处理部门的工作效率方面具有重要意义,可在海量遥感数据处理、大范围遥感监测产品生产领域发挥显著的作用。
    Abstract: As the drought occurred frequently, and remote sensing technology matures, the drought remote sensing monitoring has become an important means of drought monitoring, and the vegetation supply water index(VSWI) products is an important reference for drought monitoring, but its production involves large amount of data, and the processing cycle is long, which influence the timeliness seriously. Based on OSGI service, a distributed production model of VSWI is proposed, which turn the VSWI algorithm into an OSGI module called Bundle. This Bundle can be deployed and installed dynamically in a distributed OSGI platform, which can make full use of the resources in LAN, and be helpful to raise up the operating rate of image processing, as well as lessen the memory footprint, which have been proved in this paper. This model will play an important role in massive remote sensing data processing and emergency monitoring.
  • 图  1   遥感影像分割及覆盖示意图

    Figure  1.   Division and Cover of Remote Sensing Image

    图  2   OSGI分布式拓展架构图

    Figure  2.   Distributed Framework of OSGI

    图  3   原型系统架构图

    Figure  3.   Framework of Prototype System

    图  4   VSWI产品生成流程图

    Figure  4.   Flowchart of VSWI Product Generation

    图  5   性能对比图

    Figure  5.   Performance Comparison

    图  6   2015年7月26日长江流域旱情分布示意图

    Figure  6.   Drought of the Yangtze River Basin in July 26, 2015

    图  7   单景数据处理时间与内存占用对比图

    Figure  7.   Time Consume and Memory Size Comparison of One Scene Data Processing

    表  1   节点信息表

    Table  1   Nodes Information

    节点编号 操作系统 处理器 内存
    节点A (服务器) Windows64 AMDⅡ×4 955 4 GB
    节点B Windows32 Intel(R) i3-M330 2.31 GB
    节点C Windows64 Intel(R) i7-4510U 8 GB
    节点D Windows64 Intel(R) i7-3770 4 GB
    下载: 导出CSV

    表  2   分幅执行结果

    Table  2   Results of Partitioning

    影像序号 影像大小/MB 分幅编号 响应时间/s 执行时间/s 内存占用/MB
    2014-10-07.440 368 1_440 0.226 20.303 65.6
    2_440 0.333 28.872 43.8
    3_440 0.382 14.158 35.8
    4_440 1.075 6.794 39.1
    2014-10-07.445 369 1_445 0.299 24.146 68.8
    2_445 0.570 26.616 45.9
    3_445 0.251 14.700 37.2
    4_445 0.836 7.340 38.8
    2014-10-21.315 343 1_315 0.240 16.466 61.5
    2_315 0.685 23.164 41.1
    3_315 0.209 13.017 36.1
    4_315 0.891 6.219 38.3
    下载: 导出CSV

    表  3   ODPM模型与传统方法对比结果

    Table  3   Results Comparison of ODPM and Traditional Method

    研究区域 数据/景 ODPM模型 传统方法
    总耗时/min 内存占用/MB 总耗时/min 内存占用/MB
    太湖流域 2 21.87 273.7 100.35 646.0
    淮河流域 4 44.78 273.7 135.05 646.0
    海河流域 6 78.77 273.7 197.55 988.3
    珠江流域 9 96.27 273.7 252.27 646.0
    黄河流域 9 117.23 273.7 256.90 988.3
    长江流域 10 117.88 273.7 349.75 767.7
    松辽流域 11 229.62 338.5 432.40 1 081.6
    下载: 导出CSV
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  • 收稿日期:  2016-03-14
  • 发布日期:  2018-05-04

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