Large-Scale Combined Adjustment of Optical Satellite Imagery and ICESat-2 Data Through Terrain Profile Elevation Sequence Similarity Matching

Abstract

Earth observation utilizes multisource satellite data to enhance photogrammetry mapping. This study introduces a novel method to improve the geometric positioning accuracy of large-scale optical satellite imagery by combined adjustment with NASA's Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) laser altimetry data. Although ICESat-2 is known for its high vertical accuracy, its potential to improve horizontal accuracy has been limited due to the difficulty in matching caused by temporal inconsistencies across large survey areas. To address this, our method employs a robust terrain profile elevation sequence similarity matching technique, refined with two-dimensional Gaussian fitting to achieve enhanced position extraction. We also propose a weighted adjustment strategy that uses matching confidence to enhance the precision of the combined adjustments. Large-scale tests across various terrains showed that our approach has significantly reduced horizontal and vertical positioning errors to 4.3 and 1.7 m, respectively, outperforming existing methods.

Keywords

• Combined adjustment
• NASA's Ice
• Cloud
• and Land Elevation Satellite-2 (ICEsat-2)
• matching
• rational polynomial coefficients