Anterior|Siguiente

Application-oriented real-time remote sensing service technology

  • role： First author第一作者
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,Wuhan 430079, China

  • Email：drli@whu.edu.cn
  • Introduction：李德仁,1939年生,男，教授，中国科学院院士，中国工程院院士，国家欧亚科学院院士，研究方向为以RS、GPS和GIS为代表的空间信息科学的科研教学。E-mail: drli@whu.edu.cn
LI Deren12,  
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

DING Lin1,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,Wuhan 430079, China

SHAO Zhenfeng2

resumen

The aerospace industry in China has developed rapidly over the past 40 years. Remote sensing satellites have grown and gradually developed, and this situation has formed a variety of various satellite series. The communication satellite series, navigation satellite series, Earth observation satellite series, and science and technology test satellite series constitute the current application satellite system in China, which is used in national land and resources survey, meteorological services, environmental monitoring, ocean remote sensing, and other fields. This system provides the foundation for the development of satellite applications.At present, the satellite systems of communication, navigation, and remote sensing are separate and independent. Thus, they cannot meet the real-time, intelligent, and diversified requirements in the era of big data. In addition to the application of land, surveying and mapping, planning, geology and mining, agriculture, transportation, marine, and other industries, the government, enterprises, and the public have shown a large and urgent need for satellite remote sensing navigation data and services, especially high-resolution satellite remote sensing data and services. The demand of users in different industries and fields for remote sensing data products has gradually changed from singularity and standardization to diversification and specialization; from static investigation to dynamic monitoring, forecasting, and forecasting; from qualitative analysis to quantitative research; and from general application to batch business. Therefore, with the application requirements as the traction, focusing on improving the service capabilities of satellite remote sensing, the communication-navigation-remote sensing integrated space-based information service system with integrated communication, navigation, and remote sensing can be built to provide all-day, all-weather, all-region application-oriented services. This system fully utilizes the benefits of satellite applications. Thus, it breaks the barriers of satellites of communication, navigation, and remote sensing.Building a space-based information real-time service system (positioning, navigation, timing, remote sensing, communication, PNTRC) based on “one-satellite multitasking, multi-satellite networking, multi-network integration, and intelligent services” that integrates communication, navigation, and remote sensing, has become an important direction for the development of contemporary aerospace information technology. In the era of 5G, Internet of Things, big data and artificial intelligence, it is inevitable to study remote sensing service technologies for real-time applications in the era of 5G, Internet of Things, big data, and artificial intelligence is important to meet people’s strong demand for “fast, accurate, and flexible” remote sensing information services with the way of B2B, B2G, or B2C.This paper first studied the real-time service capabilities of space-air-ground-sea integrated Earth observation network. Then, the real-time application service requirements of remote sensing technology (requirements for high-efficiency remote sensing services for earthquake and disaster relief, remote sensing monitoring requirements for land surface deformation, and demand for public real-world services) were elaborated. Finally, the on-orbit processing technology based on artificial intelligence was analyzed, and the trend of remote sensing technology transformed from remote sensing information to real-time services was discussed.

palabra clave

remote sensing;space-air-ground-sea integrated earth observation network;real-time application services;artificial intelligence on-orbit processing

References

  1. 1.
    Chen B Y, Li X Y, Zhang G X, Guo Q, Wu Y P, Wang B Y and Chen F S. 2020. On-orbit installation matrix calibration and its application on AGRI of FY-4A. Journal of Applied Remote Sensing, 14(2): 024507
  2. 2.
    Chen M, Tomás R, Li Z H, Motagh M, Li T, Hu L Y, Gong H L, Li X J, Yu J and Gong X L. 2016. Imaging land subsidence induced by groundwater extraction in Beijing (China) using satellite radar interferometry. Remote Sensing, 8(6): 468
  3. 3.
    Dong Y, Fan D Z, Ma Q H, Ji S and Ouyang H. 2019. Automatic on-orbit geometric calibration framework for geostationary optical satellite imagery using open access data. International Journal of Remote Sensing, 40(16): 6154-6184
  4. 4.
    Feng W Q, Sui H G, Tu J H, Sun K M and Huang W M. 2017. Change detection method for high resolution remote sensing images using random forest. Acta Geodaetica et Cartographica Sinica, 46(11): 1880-1890
  5. 5.
    Goodchild M F. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4): 211-221
  6. 6.
    Jiao T Z, Hu Y X, Lv P, Zhang K and Tai X Q. 2020. A method of multi-ship target detection and tracking by on-orbit satellite. Journal of University of Chinese Academy of Sciences, 37(3): 368-378
  7. 7.
    Li D R. 2012. On Space-air-ground integrated earth observation network. Journal of Geo-information Science, 14(4): 419-425
  8. 8.
    Li D R. 2016a. Towards geo-spatial information science in Big Data Era. Acta Geodaetica et Cartographica Sinica, 45(4): 379-384
  9. 9.
    Li D R. 2016b. The “Internet Plus” space-based information services. Journal of Remote Sensing, 20(5): 708-715
  10. 10.
    Li D R. 2017. Construction of space-based information real-time service system. People’s Tribune, (18): 26-27
  11. 11.
    Li D R. 2018. Brain cognition and spatial cognition: on integration of geo-spatial big data and artificial intelligence. Geomatics and Information Science of Wuhan University, 43(12): 1761-1767
  12. 12.
    Li D R. 2019. Towards geospatial information technology in 5G/6G era. Acta Geodaetica et Cartographica Sinica, 48(12): 1475-1481
  13. 13.
    Li D R, Shao Z F, Yu W B, Zhu X Y and Zhou S H. 2020. Public epidemic prevention and control services based on big data of spatiotemporal location make cities more smart. Geomatics and Information Science of Wuhan University, 45(4): 475-487, 556
  14. 14.
    Li D R, Tong Q X, Li R X, Gong J Y and Zhang L P. 2012. Current issues in high-resolution Earth observation technology. Science China Earth Sciences, 55(7): 1043-1051
  15. 15.
    Li D R, Wang M, Shen X and Dong Z P. 2017. From earth observation satellite to earth observation brain. Geomatics and Information Science of Wuhan University, 42(2): 143-149
  16. 16.
    Li D R, Zhang G, Shen X, Zhong X, Jiang Y H, Wang T Y, Tu J G and Li Z J. 2019. Design and processing night light remote sensing of LJ-1 01 satellite. Journal of Remote Sensing, 23(6): 1011-1022
  17. 17.
    Shao Z F, Pan Y, Diao C Y and Cai J J. 2019. Cloud detection in remote sensing images based on multiscale features-convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 4062-4076
  18. 18.
    Sui H G, Liu C X, Huang L H and Hua L. 2019. Application of remote sensing technology in earthquake-induced building damage detection. Geomatics and Information Science of Wuhan University, 44(7): 1008-1019
  19. 19.
    Sui H G, Liu C X, Liu J Y, Zheng X C, Li H F, Yu S H and Li Q Y. 2020. Reflection and exploration of rapid remote sensing emergency response for typical natural disasters. Geomatics and Information Science of Wuhan University, 45(8): 1137-1145
  20. 20.
    Tang Y, Wang L J, Ma G C, Jia H J and Jin X. 2019. Emergency monitoring of high-level landslide disasters in Jinsha River using domestic remote sensing satellites. Journal of Remote Sensing, 23(2): 252-261
  21. 21.
    Wang M, Cheng Y F, Chang X L, Jin S Y and Zhu Y. 2017. On-orbit geometric calibration and geometric quality assessment for the high-resolution geostationary optical satellite GaoFen4. ISPRS Journal of Photogrammetry and Remote Sensing, 125: 63-77
  22. 22.
    Wang M, Cheng Y F, Tian Y, He L X and Wang Y L. 2018. A new on-orbit geometric self-calibration approach for the high-resolution geostationary optical satellite GaoFen4. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5): 1670-1683
  23. 23.
    Wang M and Yang F. 2019. Intelligent remote sensing satellite and remote sensing image real-time service. Acta Geodaetica et Cartographica Sinica, 48(12): 1586-1594
  24. 24.
    Wang Y L, Wang M and Zhu Y. 2020. On-Orbit Calibration of installation parameter of multiple star sensors system for optical remote sensing satellite with ground control points. Remote Sensing, 12(7): 1055
  25. 25.
    Yu S H, Yu Y B, He X J, Lu M, Wang P, An X D and Fang X C. 2020. On-board fast and intelligent perception of ships with the “Jilin-1” spectrum 01/02 satellites. IEEE Access, 8: 48005-48014
  26. 26.
    Zhang G, Wang J Y, Jiang Y H, Zhou P, Zhao Y B and Xu Y. 2019. On-orbit geometric calibration and validation of Luojia 1-01 night-light satellite. Remote Sensing, 11(3): 264

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website