Abstract
The arrival of the era of big data for Earth observation (EO) indicates that traditional data management models have been unable to meet the needs of remote sensing data in big data environments. With the launch of the first remote sensing satellite, the volume of remote sensing data has also been increasing, and traditional data storage methods have been unable to ensure the efficient management of large amounts of remote sensing data. Therefore, a professional remote sensing big data integration method is sorely needed. In recent years, the emergence of some new technical methods has provided effective solutions for multi-source remote sensing data integration. This paper proposes a multi-source remote sensing data integration framework based on a distributed management model. In this framework, the multi-source remote sensing data are partitioned by the proposed spatial segmentation indexing (SSI) model through spatial grid segmentation. The designed complete information description system, based on International Organization for Standardization (ISO) 19115, can explain multi-source remote sensing data in detail. Then, the distributed storage method of data based on MongoDB is used to store multi-source remote sensing data. The distributed storage method is physically based on the sharding mechanism of the MongoDB database, and it can provide advantages for the security and performance of the preservation of remote sensing data. Finally, several experiments have been designed to test the performance of this framework in integrating multi-source remote sensing data. The results show that the storage and retrieval performance of the distributed remote sensing data integration framework proposed in this paper is superior. At the same time, the grid level of the SSI model proposed in this paper also has an important impact on the storage efficiency of remote sensing data. Therefore, the remote storage data integration framework, based on distributed storage, can provide new technical support and development prospects for big EO data.
Highlights
Changes in the atmosphere, ocean, land, vegetation, and other factors in Earth systems affect human activities all the time
Big EO data are obtained from Earth observation systems, such as the Global Earth Observation System of Systems (GEOSS, http://www.earthobservations.org/index.php), the European Space Agency Copernicus Open Access system, Earth Observing System Data and Information System (EOSIS, https://earthdata.nasa.gov/), and the United States Geological Survey (USGS) Global Visualization Viewer system
This framework consists of two parts: the remote sensing data are spatially partitioned according to the spatial segmentation indexing (SSI) model, and the fragmented data are automatically associated with the descriptive information system
Summary
Ocean, land, vegetation, and other factors in Earth systems affect human activities all the time. EO systems can provide continuous global multi-temporal Earth data [1]. EO systems include Earth observation satellites, airborne remote sensing systems, and EO data receiving systems, and multiple platforms for observing Earth cooperate with each other [3]. This greater system is equipped with various types of sensors, which can implement real-time observation and dynamic monitoring of the global land, atmosphere, and ocean. Global Earth observation already has the ability to acquire high-resolution and high-precision temporal data for the atmosphere, ocean, and land, and EO systems have entered the era of big EO data. Big EO data are obtained from Earth observation systems, such as the Global Earth Observation System of Systems (GEOSS, http://www.earthobservations.org/index.php), the European Space Agency Copernicus Open Access system (https://scihub.copernicus.eu/), Earth Observing System Data and Information System (EOSIS, https://earthdata.nasa.gov/), and the United States Geological Survey (USGS) Global Visualization Viewer system (https://glovis.usgs.gov/)
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