The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 113–120, 2014
https://doi.org/10.5194/isprsarchives-XL-1-113-2014
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 113–120, 2014
https://doi.org/10.5194/isprsarchives-XL-1-113-2014

  07 Nov 2014

07 Nov 2014

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

P. Gao1, S. Shetty1, and H. G. Momm2 P. Gao et al.
  • 1Dept. of Electrical and Computer Engineering, Tennessee State University, Nashville, TN 37209, USA
  • 2Department of Geosciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA

Keywords: Genetic programming, feature extraction, optimal parameters

Abstract. Evolutionary computation is used for improved information extraction from high-resolution satellite imagery. The utilization of evolutionary 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. In this study, the design and implementation of a system that investigates the optimal input parameters was researched in the problem of feature extraction from remotely sensed imagery. The two primary assessment criteria were the highest fitness value and the overall computational 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) data processing: evolutionary process based on genetic programming, which is used to spectrally distinguish the features of interest from the remaining image background of remote sensed imagery. The results demonstrate that the optimal generation number is around 1500, the optimal percentage of mutation and crossover ranges from 35% to 40% and 5% to 0%, respectively. Based on our findings the sequence that yielded better results was mutation over crossover. These findings are conducive to improving the efficacy of utilizing genetic programming for feature extraction from remotely sensed imagery.