摘要
在遥感大数据的时代背景下,将遥感信息与实际生产结合,已经在各行各业得到广泛的应用。随着遥感信息处理与共享被应用到越来越多的领域,单一的遥感数据服务架构已不能满足实际生产条件中对于高可用、易扩展的要求。中国遥感卫星地面站存有海量的遥感影像数据,如何利用现有的数据来提供更好的信息服务一直是地面站探索的方向。在私有云环境下,通过kubernetes容器编排管理构建容器化的遥感信息技术处理平台,提供遥感信息服务。进而在容器化的基础环境、遥感影像计算处理、遥感数据接入以及用户服务模式4个方面展开技术研究,构建了集“数据查询获取—影像计算处理—遥感信息服务”于一体的遥感信息服务平台。
Under the background of the era of remote sensing big data,combining remote sensing information with actual production has been widely used in all walks of life.With the processing and sharing of remote sensing information being applied to more and more fields,a single remote sensing data service architecture is no longer sufficient to meet the requirements of high availability and easy expansion in actual production conditions.China remote sensing satellite ground station has a huge amount of remote sensing image data,how to use existing data to provide better information services has always been the direction of ground station exploration.In this paper,under the private cloud environment,the containerized remote sensing information technology processing platform is constructed through kubernetes container arrangement to provide remote sensing information service.Furthermore,technical research is carried out in four aspects:the containerized basic environment,remote sensing image computing and processing,remote sensing data access and user service mode.A remote sensing information service platform integrating“data query and acquisition-image calculation processing-remote sensing information service”has been constructed.
作者
闫磊
刘巍
刘士彬
段建波
夏玮
YAN Lei;LIU Wei;LIU Shibin;DUAN Jianbo;XIA Wei(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100049,China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100094,China)
出处
《中国科学院大学学报(中英文)》
CSCD
北大核心
2022年第6期793-800,共8页
Journal of University of Chinese Academy of Sciences
基金
国家重点研发计划政府间国际创新合作专项(2018YFE0100100)资助。
关键词
容器化
遥感信息服务
云平台
kubernetes容器配置管理
遥感大数据
containerization
remote sensing information service
cloud platform
kubernetes container arrangement
remote sensing big data