中南大学学报（英文版）
中南大學學報（英文版）
중남대학학보（영문판）
JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY(ENGLISH EDITION)
2014年
1期
262-271
,共10页
高红民%周惠%徐立中%石爱业
高紅民%週惠%徐立中%石愛業
고홍민%주혜%서립중%석애업
hyperspectral remote sensing images%simulated annealing genetic algorithm%support vector machine%band selection%multiple instance learning
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algorithm and multiple instance learning (MIL). The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13%at small training samples and the weaknesses of the conventional methods are overcome.
