With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it's still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.

中文翻译：

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RSIMS：大型异构遥感图像管理系统

随着地球观测技术的显着发展和进步，遥感数据保持了快速增长，其数据量已达到EB级。但是，管理和处理如此复杂且结构多样的海量遥感数据仍然是一个巨大的挑战。本文设计并实现了一种用于大规模遥感数据存储，访问和检索的分布式存储系统，称为RSIMS（遥感图像管理系统），它由三个子模块组成：RSIAPI，RSIMeta，RSIData。基于一组统一模型，不同遥感图像的结构化文本元数据都存储在RSIMeta中，然后由分布式多层Hilbert网格进行索引，以实现较高的时空检索性能。非结构化的二进制映像文件存储在RSIData中，它提供了可扩展的大存储容量和高效的GDAL（地理空间数据抽象库）兼容的I / O接口。流行的GIS软件和工具（例如QGIS，ArcGIS，rasterio）可以直接访问RSIData中存储的数据。RSIAPI为用户提供了一组用于数据访问和检索的统一接口，隐藏了RSIMS的复杂内部结构。测试结果表明，RSIMS可以存储和管理来自各种来源的大量遥感图像，并且具有高性能和稳定的性能，并且易于部署和使用。隐藏了RSIMS的复杂内部结构。测试结果表明，RSIMS可以存储和管理来自各种来源的大量遥感图像，并且具有高性能和稳定的性能，并且易于部署和使用。隐藏了RSIMS的复杂内部结构。测试结果表明，RSIMS可以存储和管理来自各种来源的大量遥感图像，并且具有高性能和稳定的性能，并且易于部署和使用。