Efficient Time-Series InSAR Data Processing via Modular Cloud-Native Parallelization
文献类型：期刊论文
| 作者 | Yu, Peichen2,3,4; Wang, Chao2,3,4; Tang, Yixian2,3,4; Zhang, Weikang1; Zou, Lichuan2,3,4; Guan, Shaoyang2,3,4; You, Haihang; Zhang, Hong2,3,4 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18页码:14240-14257 |
| 关键词 | Cloud computing Computer architecture Scalability Resource management Microservice architectures Containers Remote sensing Operating systems Elasticity Dynamic scheduling Cloud native interferometry parallelization synthetic aperture radar (SAR) |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2025.3573026 |
| 英文摘要 | Efficiently processing massive satellite datasets is a critical challenge in the field of remote sensing. The limitations of computational resources, bottlenecks in I/O operations, and pressures from data storage and transmission have long constrained the efficiency of large-scale synthetic aperture radar (SAR) data processing. Based on the concept of cloud-native computing, this study proposes a modular, multinode parallel framework for time-series interferometric SAR (InSAR) processing. The framework leverages containerization and a microservices-based architecture, incorporating multilevel parallel methods to optimize existing workflows. It achieves efficient resource allocation, alleviates I/O load, and significantly enhances data processing performance. Experimental results demonstrate that, compared to local computing resources of similar scale, this approach improves the efficiency of key processing steps by 33.1% and 16.6%, respectively. When the data volume is increased several-fold, the program's processing efficiency remains stable. The framework exhibits excellent performance under large-scale tasks and diverse computational resource scenarios, with peak CPU utilization reaching nearly 100% and memory utilization stabilizing above 80%. Moreover, it achieves high-efficiency data read/write operations across varying task scales, showcasing outstanding resource scheduling capabilities, elasticity, and scalability. This framework offers an efficient and practical solution for large-scale InSAR data processing and paves the way for broader applications of cloud-native technologies in remote sensing data analysis. |
| 资助项目 | National Natural Science Foundation of China[41930110] ; National Natural Science Foundation of China[42327801] ; Strategic Science and Technology Pioneer Program of Chinese Academy of Sciences Big Earth Data Science Engineering Project (CASEarth)[XDA19090126] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001508110200010 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42375]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wang, Chao |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Proc, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 3.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Peichen,Wang, Chao,Tang, Yixian,et al. Efficient Time-Series InSAR Data Processing via Modular Cloud-Native Parallelization[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:14240-14257. |
| APA | Yu, Peichen.,Wang, Chao.,Tang, Yixian.,Zhang, Weikang.,Zou, Lichuan.,...&Zhang, Hong.(2025).Efficient Time-Series InSAR Data Processing via Modular Cloud-Native Parallelization.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,14240-14257. |
| MLA | Yu, Peichen,et al."Efficient Time-Series InSAR Data Processing via Modular Cloud-Native Parallelization".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):14240-14257. |
入库方式： OAI收割
来源：计算技术研究所
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