The amount of remote sensing (RS) data has increased at an unexpected scale, due to the rapid progress of earth-observation and the growth of satellite RS and sensor technologies. Traditional relational databases attend their limit to meet the needs of high-resolution and large-scale RS Big Data management. As a result, massive RS data management is currently one of the most imperative topics. To address this problem, this paper describes a distributed architecture for big RS data storage based on a unified metadata file, pyramid model, and Hilbert curve for data composition and indexing using NoSQL databases (i.e, Apache Hbase). In this paper, a Hadoop-based framework in AzureInsight cloud platform is designed to manage massive RS data in a parallel and distributed way. Experimental results prove that our method has the potential to overcome the weakness of traditional methods. The proposed model is suitable for massive high-resolution image data management.
翻译:由于地球观测的迅速进步以及卫星RS和传感器技术的发展,遥感数据的数量在意外规模上增加了。传统的关系数据库在满足高分辨率和大规模RS大数据管理需要方面尽其所能。结果,大规模的RS数据管理目前是最迫切的议题之一。为了解决这一问题,本文件描述了一个分布式的大型RS数据储存结构,其基础是统一的元数据文档、金字塔模型和Hilbert曲线,用于利用NOSQL数据库(即Apache Hbase)的数据构成和索引编制。本文中,AzureInsight云平台的Hadoop框架旨在平行和分散地管理大量的RS数据。实验结果证明我们的方法有可能克服传统方法的弱点。拟议的模型适合于大规模高分辨率图像数据管理。