Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework
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 i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/6/972 |
| _version_ | 1828136545187528704 |
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| author | Yinyi Cheng Kefa Zhou Jinlin Wang Jining Yan |
| author_facet | Yinyi Cheng Kefa Zhou Jinlin Wang Jining Yan |
| author_sort | Yinyi Cheng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:06:04Z |
| format | Article |
| id | doaj.art-18ca9711837c4d0ca721709888998ae5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:06:04Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-18ca9711837c4d0ca721709888998ae52022-12-22T04:10:20ZengMDPI AGRemote Sensing2072-42922020-03-0112697210.3390/rs12060972rs12060972Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial FrameworkYinyi Cheng0Kefa Zhou1Jinlin Wang2Jining Yan3State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaThe 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.https://www.mdpi.com/2072-4292/12/6/972big earth observation dataremote sensing data integrationdistributed storagessi modelolcremote sensing metadata |
| spellingShingle | Yinyi Cheng Kefa Zhou Jinlin Wang Jining Yan Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework Remote Sensing big earth observation data remote sensing data integration distributed storage ssi model olc remote sensing metadata |
| title | Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework |
| title_full | Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework |
| title_fullStr | Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework |
| title_full_unstemmed | Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework |
| title_short | Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework |
| title_sort | big earth observation data integration in remote sensing based on a distributed spatial framework |
| topic | big earth observation data remote sensing data integration distributed storage ssi model olc remote sensing metadata |
| url | https://www.mdpi.com/2072-4292/12/6/972 |
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