Research on remote sensing image storage management and a fast visualization system based on cloud computing technology
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...
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| Veröffentlicht in: | Multimedia tools and applications 2024, Vol.83 (21), p.59861-59886 |
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| creator | Yang, Lichun He, Weibing Qiang, Xiaoyong Zheng, Jinjun Huang, Fang |
| description | 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. |
| doi_str_mv | 10.1007/s11042-023-17858-6 |
| format | Article |
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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. 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| subjects | Cloud computing Computer Communication Networks Computer Science Data base management systems Data management Data Structures and Information Theory Data transmission Digital mapping Downloading Image storage Local area networks Multimedia Information Systems Remote sensing Servers Special Purpose and Application-Based Systems Target detection |
| title | Research on remote sensing image storage management and a fast visualization system based on cloud computing technology |
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