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Current and Future Trends in Segmenting Satellite Images Using Hybrid and Dynamic Genetic Algorithms

  • Mohamad M. AwadEmail author
Chapter
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Abstract

Metaheuristic algorithms are an upper level type of heuristic algorithm. They are known for their efficiency in solving many difficult nondeterministic polynomial (NP) problems such as timetable scheduling, the traveling salesmen, telecommunications, geosciences, and many other scientific, economic, and social problems. There are many metaheuristic algorithms, but the most important one is the Genetic Algorithm (GA). What makes GA an exceptional algorithm is the ability to adapt to the problem to find the most suitable solution—that is, the global optimal solution. Adaptability of GA is the result of the population consisting of “chromosomes” which are replaced with a new one using genetics stimulated operators of crossover (reproduction ), and mutation . The performance of the algorithm can be enhanced if hybridized with heuristic algorithms. These heuristics are sometimes needed to slow the convergence of GA toward the local optimal solution that can occur with some problems, and to help in obtaining the global optimal solution. GA is known to be very slow compared to other known optimization algorithms such as Simulated Annealing (SA). This speed will further decrease when GA is hybridized (HyGA). To overcome this issue, it is important to change the structure of the chromosomes and the population . In general, this is done by creating variable length chromosomes . This type of structure is called a Hybrid Dynamic Genetic Algorithm (HyDyGA). In this chapter, GA is covered in detail, including hybridization using the Hill-Climbing Algorithm. The improvements to GA are used to solve a very complex NP problem, which is image segmentation. Using multicomponent images increases the complexity of the segmentation task and puts more burden on GA performance. The efficiency of HyGA and HyDyGA in the segmentation process of multicomponent images is proved using collected field samples; it can reach more than 97%. In addition, the reliability and the robustness of the new algorithms are proved using different analysis methods.

Keywords

Dynamic Genetic algorithm Land cover Metaheuristic Segmentation 

Notes

Acknowledgements

The author thanks CNRS and the United States Geological Survey for providing satellite images which were used to prove many concepts in this chapter.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Council for Scientific ResearchRemote Sensing CenterBeirutLebanon

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