Storage Model Design and Implementation of High Resolution and Hyperspectral Remote Sensing Image Based on NoSQL
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摘要: 如何高效存储、管理呈几何增长的高分辨率、高光谱遥感影像数据, 实现遥感数据的快速处理、检索和可视化是急需解决的问题.应用非关系型数据库技术, 设计了由遥感元数据库、遥感影像数据库以及影像金字塔3个部分组成的海量遥感影像存储模型; 建立了由硬件支撑层、数据层、数据服务层及应用层组成的遥感影像存储中间件.通过实验分析, 验证了基于非关系数据库的遥感影像数据存储模型及中间件对影像数据的读写、提取性能优于传统的关系数据库.研究成果可满足高分高光谱遥感探测与评价模型对海量影像高效存储、管理的需求, 具有重要的实用价值.Abstract: It is an issue to be addressed as how to efficiently store and manage massive data that in high resolution and high spectral remote sensing images, and achieve rapid processing, retrieval and visualization of remote sensing data is the problem to be solved. Using non-relational database technology, we designed massive image storage model consists of remote sensing metadata database, image database and image pyramid; set up the remote sensing image storage middleware that consists of the hardware support layer, data layer, data service layer, and application layer. Experiment verifies the remote sensing image data storage model and middleware that based on non-relational database is superior to relational database for video data read and write. Research results prove have great practical value for efficiently storage and management of massive remote sensing image data.
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Key words:
- remote sensing image /
- not only structured query language(NoSQL) /
- metadata /
- pyramid
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图 3 影像数据分块管理及其存储模型
①影像分块；②影像切片存储；③切片元数据存储
Fig. 3. Image data block management and storage model
图 4 基于NoSQL的遥感影像存储中间件系统总体架构
Fig. 4. The system architecture of storage middleware based on NoSQL for remote sensing image
图 6 影像数据入库效率(a)和影像数据出库效率(b)
Fig. 6. Efficiency of image data storage (a) and efficiency of image data outbound (b)
表 1 不同大小影像数据集入库/出库平均时间
Table 1. Average time of different size image data collection of inbound/outbound
数据集(MB) Couchbase Oracle 87.5 入库时间：5.695 入库时间：17.254 出库时间：5.747 出库时间：14.704 351.0 入库时间：21.258 入库时间：61.686 出库时间：21.846 出库时间：65.031 879.0 入库时间：55.289 入库时间：187.422 出库时间：55.151 出库时间：186.133 1 167.0 入库时间：67.600 入库时间：247.507 出库时间：66.910 出库时间：217.216 1 310.0 入库时间：80.624 入库时间：273.341 出库时间：83.906 出库时间：340.847 2 048.0 入库时间：124.913 入库时间：529.176 出库时间：164.234 出库时间：614.513 
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