Volume 47 Issue 8
Aug.  2022
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WU Tianjun, LUO Jiancheng, ZHAO Xin, LI Manjia, ZHANG Xin, DONG Wen, GAO Lijing, WANG Lingyu, YANG Yingpin, ZHAO Wei. Collaborative Computing of High-Resolution Remote Sensing Driven by Fine-Accurate Geographic Applications[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1220-1235. DOI: 10.13203/j.whugis20220335
Citation: WU Tianjun, LUO Jiancheng, ZHAO Xin, LI Manjia, ZHANG Xin, DONG Wen, GAO Lijing, WANG Lingyu, YANG Yingpin, ZHAO Wei. Collaborative Computing of High-Resolution Remote Sensing Driven by Fine-Accurate Geographic Applications[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1220-1235. DOI: 10.13203/j.whugis20220335
shu

Collaborative Computing of High-Resolution Remote Sensing Driven by Fine-Accurate Geographic Applications

  • 1.

    School of Science, Chang'an University, Xi'an 710064, China

  • 2.

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • 3.

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • 4.

    School of Geography and Environmental Science, Institute of Karst Science, Guizhou Normal University, Guiyang 550001, China

  • 5.

    School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China

  • 6.

    Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

Funds: 

The National Natural Science Foundation of China 42071316

the National Key Research and Development Program of China 2021YFB3900905

the National Key Research and Development Program of China 2021YFB3901300

Major Science and Technology Project of Inner Mongolia Autonomous Region 2021ZD0045

Digital Map Project of Chongqing Agricultural Industry 21C00346

Key Research and Development Program of Shaanxi Province 2021NY-170

Fundamental Research Funds for the Central Universities, CHD 300102120201

More Information
  • Author Bio:

    WU Tianjun, PhD, associate professor, majors in intelligent computer and application of remote sensing big data. E‐mail: tjwu@chd.edu.cn

  • Corresponding author:

    LUO Jiancheng, PhD, professor. E-mail: luojc@aircas.ac.cn

  • Received Date: May 30, 2022
  • Available Online: August 15, 2022
  • Published Date: August 04, 2022
    Graphical Abstract
    • Abstract
  •   Objectives  Intelligent interpretation based on high-resolution remote sensing is an important way to realize the fine generation and rapid update of geographic information. Based on the background of fine-accurate geographical application using remote sensing, this paper analyzes the limitations of remote sensing information products in production and application. We explain the necessity of remote sensing serving geographical research and the key of coupling the fine shape of the map and the accurate content of the spectrum.
      Methods  Based on the basic understanding that geography guides the research of intelligent remote sensing, we first put forward the development direction and technical ideas of high-resolution remote sensing geoscience analysis based on fine geographical scenes. Then, we propose an intelligent computing mode via the space-time/satellite-ground collaboration. Furthermore, we take the evaluation of rocky desertification cultivated land in Guanling County, Guizhou Province, China as an application case.
      Results  Through this case study, three basic models, namely zoning-stratified perception, spatiotemporal synergistically inversion and multi-granular decision-making, are used to show how to carry out fine-accurate application by high-resolution remote sensing collaborative computing in complex mountain areas from a comprehensive perspective of refinement, quantification and topicalization.
      Conclusions  Combined with the previous work experience and practical cognition, several key scientific problems are discussed, such as spatial expression using irregular grids, multimodal reconstruction of temporal features, multi-source uncertainty analysis and iterative optimization guided by uncertainty. We give some potential study directions and research ideas in order to establish a more complete and feasible theoretical system and explore the development paths for the research on intelligent remote sensing under the guidance of geography and the fine-accurate geographical application with remote sensing data.
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    • References (62)
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