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Remote Sens. 2018, 10(1), 7; https://doi.org/10.3390/rs10010007

Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure

1,2
,
1,2,* , 3
and
1,2,3,*
1
School of Computer Science, China University of Geoscience, Wuhan 430074, China
2
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences Wuhan, Wuhan 430074, China
3
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Authors to whom correspondence should be addressed.
Received: 31 August 2017 / Revised: 10 December 2017 / Accepted: 20 December 2017 / Published: 21 December 2017
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Abstract

Since Landsat-1 first started to deliver volumes of pixels in 1972, the volumes of archived data in remote sensing data centers have increased continuously. Due to various satellite orbit parameters and the specifications of different sensors, the storage formats, projections, spatial resolutions, and revisit periods of these archived data are vastly different. In addition, the remote sensing data received continuously by each data center arrives at a faster code rate; it is best to ingest and archive the newly received data to ensure users have access to the latest data retrieval and distribution services. Hence, an excellent data integration, organization, and management program is urgently needed. However, the multi-source, massive, heterogeneous, and distributed storage features of remote sensing data have not only caused difficulties for integration across distributed data center spatial infrastructures, but have also resulted in the current modes of data organization and management being unable meet the rapid retrieval and access requirements of users. Hence, this paper proposes an object-oriented data technology (OODT) and SolrCloud-based remote sensing data integration and management framework across a distributed data center spatial infrastructure. In this framework, all of the remote sensing metadata in the distributed sub-centers are transformed into the International Standardization Organization (ISO) 19115-based unified format, and then ingested and transferred to the main center by OODT components, continuously or at regular intervals. In the main data center, in order to improve the efficiency of massive data retrieval, we proposed a logical segmentation indexing (LSI) model-based data organization approach, and took SolrCloud to realize the distributed index and retrieval of massive metadata. Finally, a series of distributed data integration, retrieval, and comparative experiments showed that our proposed distributed data integration and management program is effective and promises superior results. Specifically, the LSI model-based data organization and the SolrCloud-based distributed indexing schema was able to effectively improve the efficiency of massive data retrieval. View Full-Text
Keywords: multi-sourced remote sensing big data; data integration; data management; distributed data centers; OODT multi-sourced remote sensing big data; data integration; data management; distributed data centers; OODT
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Fan, J.; Yan, J.; Ma, Y.; Wang, L. Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure. Remote Sens. 2018, 10, 7.

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