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Research on remote sensing image storage management and a fast visualization system based on cloud computing technology

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Abstract

With the continuous development of remote sensing technology, the data volume of remote sensing images has increased exponentially, resulting in many difficulties in the storage, management, transmission, calculation, and other processes of remote sensing images. In order to solve the above problems, this paper studies the use of the Hadoop Distributed File System (HDFS) and related technologies to design and implement a browser/server (B/S) architecture for a massive, multisource, remote sensing images distributed storage management system. The image data are stored in the HDFS, and the image metadata are stored in a MySQL database. The distributed parallel construction of the image pyramid is completed based on the Spark computing engine, and the Akka framework is used to construct WMTS (Web Map Tile Service) to realize the release of remote sensing images. Finally, the rapid visual display of remote sensing images is carried out using Leaflet. The system also supports image data management, image target detection, user management, and other functions. After testing, this system can support the storage and management of multisource remote sensing image data, and can solve perfectly the problems of insufficient storage space and insufficient computing power of a single server. It is found that the upload and download speeds of a large amount of remote sensing images can be close to the maximum speed of a gigabit local area network (LAN). In the gigabit LAN environment, the average upload speed of a single remote sensing image is 97.74 MB/s, and the average download speed is 87.62 MB/s. In terms of image pyramid construction, the speed of a multi-node parallel construction based on Spark is two times higher than that of a single-node construction. Additionally, compared to similar systems, this system has better data transmission and retrieval speed, better data computing ability, and higher concurrency processing ability.

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Due to the nature of this research, the participants of this study do not agree for their data to be shared publicly. Hence, supporting data are not available.

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Acknowledgements

This study was mainly supported by the National Science Foundation of China (Grant No. 42271390) and the Technological Innovation R&D Project of Chengdu Science and Technology Bureau (Grant No. 2022-YF05-00967-SN). This work was partial funded by the Fundamental Research Funds for the Central Universities (Grant Nos. ZYGX2019J069 and ZYGX2019J072) and Hubei Provincial Key Laboratory of Intelligent Geo-information Processing (China University of Geosciences; Grant Nos. KLIGIP-2018A08).

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Authors and Affiliations

  1. School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China

    Lichun Yang

  2. Jiangsu Automation Research Institute, Lianyungang, Jiangsu Province, 222061, China

    Lichun Yang

  3. School of Recourses and Environment, University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan Province, 611731, China

    Weibing He, Xiaoyong Qiang, Jinjun Zheng & Fang Huang

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  1. Lichun Yang
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  2. Weibing He
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  5. Fang Huang
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Correspondence to Lichun Yang.

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Yang, L., He, W., Qiang, X. et al. Research on remote sensing image storage management and a fast visualization system based on cloud computing technology. Multimed Tools Appl 83, 59861–59886 (2024). https://doi.org/10.1007/s11042-023-17858-6

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  • DOI: https://doi.org/10.1007/s11042-023-17858-6

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