Metaheuristics for Supervised Parameter Tuning of Multiresolution Segmentation

Victor Andres Ayma Quirita, Pedro Achanccaray Diaz, Raul Q. Feitosa, Patrick N. Happ, Gilson A.O.P. Costa, Tobias Klinger, Christian Heipke

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

9 Citas (Scopus)


This letter evaluates metaheuristics for the supervised parameter tuning of multiresolution-region-growing segmentation. Three groups of metaheuristics are tested in terms of convergence speed and solution quality. Generalized pattern search, mesh adaptive direct search, and Nelder-Mead represent the single-solution group. Differential evolution (DE) represents the population group. DE followed by each of the aforementioned single-solution metaheuristics represents the hybrid metaheuristic group. This letter reveals that the optimization objective functions typically have countless local minima, many of them leading to very poor solutions. Experiments on three data sets demonstrated that single-solution-based methods often lead to a solution with unacceptable quality. DE was less susceptible to be stuck in local minima when compared to single-solution methods, but it was slower in reaching the minima. Moreover, hybrid methods presented the best tradeoff between accuracy and convergence speed.

Idioma originalInglés
Número de artículo7519028
Páginas (desde-hasta)1364-1368
Número de páginas5
PublicaciónIEEE Geoscience and Remote Sensing Letters
EstadoPublicada - set. 2016
Publicado de forma externa


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