Abstract
Abstract. Rational Function Models (RFM) are one of the most considerable approaches for spatial information extraction from satellite images especially where there is no access to the sensor parameters. As there is no physical meaning for the terms of RFM, in the conventional solution all the terms are involved in the computational process which causes over-parameterization errors. Thus in this paper, advanced optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are investigated to determine the optimal terms of RFM. As the optimization would reduce the number of required RFM terms, the possibility of using fewer numbers of Ground Control Points (GCPs) in the solution comparing to the conventional method is inspected. The results proved that both GA and PSO are able to determine the optimal terms of RFM to achieve rather the same accuracy. However, PSO shows to be more effective from computational time part of view. The other important achievement is that the algorithms are able to solve the RFM using less GCPs with higher accuracy in comparison to conventional RFM.
Highlights
Nowadays, due to the availability of High Resolution Satellite Images (HRSIs), accurate geospatial information could be extracted from those types of images
The possibility of using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are investigated to find the optimal combination of coefficients which may lead to eliminate over-parameterization errors, to reduce Ground Control Points (GCPs) and to rectify HRSIs with more accuracy
Both GA and PSO when using for Rational Function Models (RFM) optimization, can achieve sub-pixel accuracy even with just 4 GCPs
Summary
Due to the availability of High Resolution Satellite Images (HRSIs), accurate geospatial information could be extracted from those types of images This information can be used in different applications such as image matching, image registration, ortho-rectification, mapping and so on. The fundamental problem to use the HRSIs in rigorous models is disinclination of some HRSIs vendors such as GeoEye to expose the ephemeris data and interior orientation parameters of satellites. These models are sensor dependent and the solution of them has much complexity. Both GA and PSO when using for RFM optimization, can achieve sub-pixel accuracy even with just 4 GCPs
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
