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Published： 07 March 2023 ，
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张正，李宏益，胡昌苗，唐娉.2023.高分遥感共性产品生产系统关键设计.遥感学报，27（3）： 651-664
Zhang Z，Li H Y，Hu C M and Tang P. 2023. GF quantitative remote sensing production system： Core design. National Remote Sensing Bulletin， 27（3）：651-664
以众多遥感卫星的发射与行业应用系统为代表，遥感应用技术在近年间取得了快速发展，国产高分系列卫星等平台所提供的数据越来越丰富，以遥感共性产品作为信息源的应用系统也已经覆盖了多个行业。然而这两者之间的桥梁，即由国产卫星数据生产标准化系列遥感共性产品的能力，仍显得相对薄弱，极大限制了国产遥感数据的使用率与影响力。这一能力的改善需要从产品体系、产品算法与生产系统等多方面共同推进。本文从高分遥感共性产品生产系统的角度，针对系统所面对的来自数据集成、算法集成、生产编排与云计算等方面的诸多挑战，提出了一套完整的系统关键设计。系统在容器化集成运行的基础上，涵盖了集群、数据、算法、权限、参数、工作流、生产等一系列关键环节的关键技术。系统研发与生产实践表明，本系统可以流畅的集成各类共性产品算法，实现稳定高效的业务化生产平台，助力于国产卫星产品服务能力的有效提高。
A fast development in remote sensing science and technologies has been witnessed in recent years with the launch of various remote sensing satellites and the establishment of numerous industrial application systems. Satellite series
like GaoFen
has pushed the richness of data to a new level. Moreover
quantitative product-driven application systems have become increasingly influential in many disciplines. By contrast
the bridge between data and application
namely
the production capability of quantitative products
seems weak
greatly limiting the usage and influence of GaoFen data. The improvement of production capability comes from multiple aspects
including product hierarchy
algorithm model
and production system. In this study
we propose
from the production system perspective
an integrated system design that responds to the four main challenges of the system: uniform data access
heterogeneous algorithm integration
layered workflow orchestration
and cloud infrastructure adaptation.
The system is based on algorithm containerization
where executable algorithms and all their dependencies are encapsulated. Thus
it can run uniformly and consistently on different infrastructures without worrying about the complexity caused by deployment. This scenario helps the system to manage diverse remote sensing algorithms uniformly. We employ the Kubernetes container orchestration platform to automate the execution
scaling
and management of containerized algorithms. A containerized cluster consists of multiple master nodes
many computing nodes
and multiple data centers. Multiple algorithm repositories are constructed to support the system and cope with the high computing and data throughput density of remote sensing algorithms. Each algorithm repository is further divided into several subrepositories to improve load balancing. User-defined role-based access control for algorithms is set up to protect the intellectual properties of algorithm owners. A recommended algorithm image architecture is introduced to standardize algorithm encapsulation. A set of nine properties are abstracted to describe uniformly any data entity parameter of an algorithm. This approach ensures that suitable input data can be found for user-uploaded algorithms to run in the system. For data visualization and quantitative computing scenarios
a multiscenario data organization strategy is proposed to avoid excessive data operations
such as projection transform or subdivision. The business logic of the system
from user order creation to product calculation
is detailed for clear implementation. The production sometimes involves workflow batches. We propose a stratified workflow aggregation strategy to optimize workflow execution.
The system has been used for large-scale production of various GF quantitative remote sensing products
including surface reflectance product
normalized difference vegetation index product
leaf area index product
and surface albedo product. These products fully cover China’s area for eight successive years from 2013 to 2020
with quantities of more than five million and storages of nearly 300 TB. The proposed system completes the production task smoothly and efficiently.
During the routine support for many large-scale production tasks
each part of the system performed consistently with the system design proposed in this study
demonstrating that the study can help build a stable and efficient quantitative remote sensing production system on cloud-native infrastructures
生产系统共性产品算法集成容器云计算系统架构工作流
production systemquantitative productalgorithm integrationcontainercloud computingsystem architectureworkflow
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