Research on classification of GF satellite data application products and construction of common product system

被引:0
作者
Liao C. [1 ]
Zhao J. [1 ]
Xing J. [1 ]
Wen J. [2 ]
Liu Q. [2 ]
机构
[1] Earth Observation System and Data Center, China National Space Administration, Beijing
[2] State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academic of Sciences, Beijing
关键词
authenticity inspection system; common product; GF satellite; product classification; product system; thematic product;
D O I
10.11834/jrs.20232593
中图分类号
学科分类号
摘要
The major special deployment of the High Resolution Earth observation system is to build a national validation system for common products. The purpose is to form the ability to verify the authenticity of high resolution common products, build the mass production capacity of common products, and improve the overall level of quantitative application of high resolution remote sensing in China. Therefore, how to define common products, what are common products, what are the generating relationships between common products, and between common products and thematic products is a problem that must be solved first. Focusing on these problems, this paper proposes the overall architecture of the national authenticity inspection system for high resolution special projects, that is, the bottom layer is the authenticity inspection station network observation subsystem, building a authenticity inspection station network composed of 42 stations, obtaining the actual authenticity inspection data through sensors, unmanned aerial vehicles and manual measurement, and collecting them in the system database of the middle layer in real time. The middle layer is a subsystem of authenticity inspection and finalization of common products, with integrated data management and sharing, authenticity inspection station network management, authenticity inspection of common products, algorithm model integration testing and finalization of common products and other functions. The top layer is the national remote sensing data and application service platform, which is the portal for authenticity testing. This paper defines the category and grading method of high resolution common products, that is, the standard products are divided into 0—2 levels, which refer to the original image data generated by the special ground system for high resolution, or the image data after system geometric correction and/or radiometric correction; Common products are divided into 3 to 5 levels, which refers to the data obtained by the high resolution special application system using quantitative remote sensing feature parameter inversion method based on standard products as the production input of at least two industry specific products; Thematic products are classified into 6—7 levels, which refer to the data supporting the business application of the industry and department obtained by professional users using quantitative remote sensing characteristic parameter inversion method or multi-source data superposition analysis method based on standard products or generic products. The paper discusses the construction process of the generic product system. Taking agriculture and transportation as examples, it analyzes and deduces the demand for generic products in the production of industry specific products. Furthermore, it deduces the derivative relationship between generic products in the form of product tree, and clarifies the inspection object of the authenticity inspection system and the product catalog that should be focused on, which has important guiding significance for the implementation of the system. After the completion and operation of the national authenticity inspection system, in a sense, it will become a “challenge arena” for common product algorithms. Any algorithm that has been proved to be better through algorithm evaluation will be integrated into the system to replace the original algorithm. These algorithms will also further update the generation process of existing common products iteratively, which will promote each other. © 2023 National Remote Sensing Bulletin. All rights reserved.
引用
收藏
页码:563 / 572
页数:9
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