Paper Info
Title
A New Combined Adjustment Model For Geolocation Accuracy Improvement Of Multiple Sources Optical And Sar Imagery
Abstract
Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.
Year
DOI
Venue
2021
10.3390/rs13030491
REMOTE SENSING
Keywords
DocType
Volume
combined adjustment, rational function model (RFM), range-doppler (RD) model, multiple sources
Journal
13
Issue
Citations 
PageRank 
3
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Niangang Jiao121.74
Feng Wang200.68
Hongjian You310317.44