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Article

Efficient Management and Scheduling of Massive Remote Sensing Image Datasets

by
Jiankun Zhu
1,
Zhen Zhang
1,*,
Fei Zhao
2,
Haoran Su
3,
Zhengnan Gu
1 and
Leilei Wang
1
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
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2023, 12(5), 199; https://doi.org/10.3390/ijgi12050199
Submission received: 20 February 2023 / Revised: 2 May 2023 / Accepted: 12 May 2023 / Published: 13 May 2023
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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.
Keywords: remote sensing; distributed storage system; big data; scheduling optimization remote sensing; distributed storage system; big data; scheduling optimization

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MDPI and ACS Style

Zhu, J.; Zhang, Z.; Zhao, F.; Su, H.; Gu, Z.; Wang, L. Efficient Management and Scheduling of Massive Remote Sensing Image Datasets. ISPRS Int. J. Geo-Inf. 2023, 12, 199. https://doi.org/10.3390/ijgi12050199

AMA Style

Zhu J, Zhang Z, Zhao F, Su H, Gu Z, Wang L. Efficient Management and Scheduling of Massive Remote Sensing Image Datasets. ISPRS International Journal of Geo-Information. 2023; 12(5):199. https://doi.org/10.3390/ijgi12050199

Chicago/Turabian Style

Zhu, Jiankun, Zhen Zhang, Fei Zhao, Haoran Su, Zhengnan Gu, and Leilei Wang. 2023. "Efficient Management and Scheduling of Massive Remote Sensing Image Datasets" ISPRS International Journal of Geo-Information 12, no. 5: 199. https://doi.org/10.3390/ijgi12050199

APA Style

Zhu, J., Zhang, Z., Zhao, F., Su, H., Gu, Z., & Wang, L. (2023). Efficient Management and Scheduling of Massive Remote Sensing Image Datasets. ISPRS International Journal of Geo-Information, 12(5), 199. https://doi.org/10.3390/ijgi12050199

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