Learning object filters for high resolution satellite images using genetic algorithms

This paper introduces a novel methodology for texture object detection using genetic algorithms. The method employs a kind of high performance detection filter defined as 2D masks, which are derived using genetic algorithm operating. The population of filters iteratively evaluated according to a statistical performance index corresponding to object detection ability, and evolves into an optimal filter using the evolution principles of genetic search. Experimental results of texture object detection in high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.