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Evolutionary computation is used for improved information extraction from high-resolution satellite imagery. The utilization ofevolutionary computation is based on stochastic selection of input parameters often defined in a trial-and-error approach. However,exploration of optimal input parameters can yield improved candidate solutions while requiring reduced computation resources. Inthis study, the design and implementation of a system that investigates the optimal input parameters was researched in the problem offeature extraction from remotely sensed imagery. The two primary assessment criteria were the highest fitness value and the overallcomputational time. The parameters explored include the population size and the percentage and order of mutation and crossover.The proposed system has two major subsystems; (i) data preparation: the generation of random candidate solutions; and (ii) dataprocessing: evolutionary process based on genetic programming, which is used to spectrally distinguish the features of interest fromthe remaining image background of remote sensed imagery. The results demonstrate that the optimal generation number is around1500, the optimal percentage of mutation and crossover ranges from 35% to 40% and 5% to 0%, respectively. Based on our findingsthe sequence that yielded better results was mutation over crossover. These findings are conducive to improving the efficacy ofutilizing genetic programming for feature extraction from remotely sensed imagery

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Exploration of Genetic Programming Optimal Parameters for Feature Extraction from Remote Sensed Imagery