Efficient Management and Scheduling of Massive Remote Sensing Image Datasets
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Published:2023-05-13
Issue:5
Volume:12
Page:199
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ISSN:2220-9964
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Container-title:ISPRS International Journal of Geo-Information
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language:en
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Short-container-title:IJGI
Author:
Zhu Jiankun1, Zhang Zhen1ORCID, Zhao Fei2, Su Haoran3, Gu Zhengnan1, Wang Leilei1
Affiliation:
1. School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China 2. China Satellite Communications Co., Ltd., Beijing 100190, China 3. School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Abstract
The rapid development of remote sensing image sensor technology has led to exponential increases in available image data. The real-time scheduling of gigabyte-level images and the storage and management of massive image datasets are incredibly challenging for current hardware, networking and storage systems. This paper’s three novel strategies (ring caching, multi-threading and tile-prefetching mechanisms) are designed to comprehensively optimize the remote sensing image scheduling process from image retrieval, transmission and visualization perspectives. A novel remote sensing image management and scheduling system (RSIMSS) is designed using these three strategies as its core algorithm, the PostgreSQL database and HDFS distributed file system as its underlying storage system, and the multilayer Hilbert spatial index and image tile pyramid to organize massive remote sensing image datasets. Test results show that the RSIMSS provides efficient and stable image storage performance and allows real-time image scheduling and view roaming.
Funder
Major Project on Natural Science Foundation of Universities in Anhui Province National Natural Science Foundation of China
Subject
Earth and Planetary Sciences (miscellaneous),Computers in Earth Sciences,Geography, Planning and Development
Reference41 articles.
1. The Research for the Management and Distribution Integration of MultiR-Rsource Remote Sensing Image;Zhou;Geomat. Spat. Inf. Technol.,2017 2. Fan, J., Yan, J., Ma, Y., and Wang, L. (2017). Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure. Remote Sens., 10. 3. Gomes, V.C.F., Queiroz, G.R., and Ferreira, K.R. (2020). An Overview of Platforms for Big Earth Observation Data Management and Analysis. Remote Sens., 12. 4. A Raster Tile Calculation Model Combined with Map Service;Hu;J. Geo-Inf. Sci.,2021 5. Barclay, T., Eberl, R., Gray, J., Nordlinger, J., Raghavendran, G., Slutz, D., Smith, G., Smoot, P., Hoffman, J., and Robb, N. (1998). Microsoft TerraServer. arXiv.
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