Introducing genetic modification concept to optimize rational function models (RFMs) for georeferencing of satellite imagery

Abstract

Genetic algorithms (GAs) are frequently used for optimization of remote sensing models. Recently, they have been used in optimization of rational function models (RFMs) for georeferencing of satellite images. In this way, fewer ground control points (GCPs) are needed while accurate results are achieved in comparison to manual or try and error based approaches of terrain dependent RFM term selection. However, GAs are quite inefficient in terms of computational speed. In this article, a novel optimization approach adopting a newly introduced concept in natural sciences called genetic modification is proposed to speed up the basic GA. According to the proposed method, a qualification coefficient is defined to examine the qualification of individual genes. Therefore, qualified genes are identified and are used to produce a new set of chromosomes in each iteration of the algorithm as transgenic chromosomes. Considering these chromosomes as a part of parents for next generation, desired characteristics (optimal parameters) appeared with an efficient speed. To evaluate the performance of the proposed algorithm, over two different case studies, RFM is optimized using both proposed and basic GAs. The results indicate that the optimization speed is improved by 20 times, while the accuracies are preserved.