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Method for Improving Positioning Accuracy of Rotating Scanning Satellite Images via Multi-Source Satellite Data

Liwei Wang1,2, Peng Wang3, Yamin Zhang1,2

  • 1School of Aerospace, Harbin Institute of Technology, Shenzhen 518055, China.

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This study introduces a precise positioning method for rotating scanning satellite imagery using multi-source data fusion. The approach significantly improves accuracy by correcting geometric distortions, achieving meter-level positioning.

Keywords:
RPC modelgeometric positioningjoint adjustmentrotating scanning

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Area of Science:

  • Remote Sensing
  • Photogrammetry
  • Geodesy

Background:

  • Rotating scanning systems acquire ultra-wide swath imagery but face positioning accuracy issues due to geometric distortions.
  • Difficulty in obtaining ground control points (GCPs) over large areas exacerbates accuracy degradation.

Purpose of the Study:

  • To develop a precise positioning method for rotating scanning satellite imagery.
  • To address geometric distortions and improve positioning accuracy through multi-source data fusion.

Main Methods:

  • Multi-source satellite data fusion (ZY-3, GF-2, DEM).
  • Grid-based feature point extraction and high-precision matching.
  • Integration of geometric constraints with Least Squares Minimization (LSM) and a robust joint adjustment model.

Main Results:

  • Achieved planar accuracy of 4.01 m for imagery after joint adjustment.
  • Edge matching Root Mean Square Error (RMSE) between adjacent images was 2.52 m.
  • Cooperative positioning accuracy reached 4.68 m in mountainous and 5.22 m in plain areas.

Conclusions:

  • The multi-source cooperative adjustment effectively corrects geometric distortions in rotating scanning imagery.
  • The proposed method significantly enhances positioning accuracy, meeting meter-level requirements.