Efficient Time-Series InSAR Data Processing via Modular Cloud-Native Parallelization

doi:10.1109/JSTARS.2025.3573026

	Efficient Time-Series InSAR Data Processing via Modular Cloud-Native Parallelization
	Yu, Peichen 1,2,3; Wang, Chao 1,2,3; Tang, Yixian 1,2,3; Zhang, Weikang 4; Zou, Lichuan 1,2,3; Guan, Shaoyang 1,2,3; You, Haihang ; Zhang, Hong 1,2,3
	2025
发表期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
卷号	18 页码:14240-14257
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.
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)
DOI	10.1109/JSTARS.2025.3573026
收录类别	SCI
语种	英语
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
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001508110200010
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42376
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, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 2.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, State Key Lab Proc, Beijing 100190, 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.