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

## TL;DR

This paper introduces a method to improve the positioning accuracy of rotating scanning satellite images by fusing data from multiple satellite sources.

## Contribution

The novel approach uses grid-based feature extraction and joint adjustment to achieve meter-level accuracy without dense ground control points.

## Key findings

- The framework achieved a planar accuracy of 4.01 m and edge matching RMSE of 2.52 m using ZY-3 and GF-2 imagery.
- Meter-level positioning accuracy (4.68 m in mountainous areas and 5.22 m in plains) was achieved for simulated ultra-wide rotating scanning imagery.
- Multi-source fusion effectively corrects geometric distortions and improves positioning accuracy in rotating scanning systems.

## Abstract

What are the main findings?
A multi-source collaborative positioning framework integrates ZY-3 and GF-2 imagery to achieve a planar accuracy of 4.01 m and an edge matching RMSE of 2.52 m.The proposed grid-based feature extraction and joint adjustment method successfully attains meter-level positioning accuracy (4.68 m and 5.22 m) for simulated ultra-wide rotating scanning imagery.

A multi-source collaborative positioning framework integrates ZY-3 and GF-2 imagery to achieve a planar accuracy of 4.01 m and an edge matching RMSE of 2.52 m.

The proposed grid-based feature extraction and joint adjustment method successfully attains meter-level positioning accuracy (4.68 m and 5.22 m) for simulated ultra-wide rotating scanning imagery.

What are the implications of the main findings?
The approach effectively mitigates complex geometric distortions in rotating scanning systems without dense ground control points or strict physical models.This study provides a robust solution for generating seamless, high-precision orthophoto products from ultra-wide swath satellite data using multi-source fusion.

The approach effectively mitigates complex geometric distortions in rotating scanning systems without dense ground control points or strict physical models.

This study provides a robust solution for generating seamless, high-precision orthophoto products from ultra-wide swath satellite data using multi-source fusion.

Rotating scanning systems are capable of acquiring ultra-wide swath satellite imagery, but they suffer from significant positioning accuracy degradation due to complex geometric distortions and the difficulty of obtaining ground control points (GCPs) over vast areas. To address these issues, this paper proposes a precise positioning method based on multi-source satellite data fusion. By comprehensively utilizing high-resolution images from ZY-3 and GF-2 satellites alongside DEM data, we establish a framework that integrates grid-based feature point extraction, high-precision matching, and multi-image joint adjustment. Specifically, we introduce a matching strategy combining geometric constraints with Least Squares Minimization (LSM) and a robust joint adjustment model to suppress geometric distortions. Experimental validation was conducted using a dataset covering the Beijing area. The results demonstrate that after joint adjustment, the planar accuracy of the imagery reached 4.01 m, and the edge matching Root Mean Square Error (RMSE) between adjacent images was 2.52 m. Furthermore, the cooperative positioning accuracy for segmented simulation data achieved 4.68 m in mountainous areas and 5.22 m in plain areas, meeting the requirements for meter-level positioning. These results verify the effectiveness of multi-source cooperative adjustment in correcting geometric distortions and significantly improving the positioning accuracy of rotating scanning imagery.