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In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy.<\/jats:p>","DOI":"10.1155\/2018\/6595792","type":"journal-article","created":{"date-parts":[[2018,1,18]],"date-time":"2018-01-18T18:43:23Z","timestamp":1516301003000},"page":"1-13","source":"Crossref","is-referenced-by-count":20,"title":["Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search"],"prefix":"10.1155","volume":"2018","author":[{"given":"Lei","family":"Shi","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"}]},{"given":"Youchuan","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, 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