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

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, multi-node parallel framework for time-series InSAR processing. The framework leverages containerization and a microservices-based architecture, incorporating multi-level 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.