Application of Messy Genetic Algorithm (GA) to satellite image clustring

Abstract

Messy GA is applied to the satellite image clustering. Messy GA allows to maintain a long schema, due to the fact that schema can be expressed with a variable length of codes, so that more suitable cluster can be found in comparison to the existing Simple GA clustering. The results with simulation data show that tha proposed Messy GA based clustering shows four time better cluster separability in comparison to the Simple GA while the results with Landsat TM data of Saga show that almost 65% better clustring performance.