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Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning

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Abstract

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.

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Authors and Affiliations

  1. College of Computer and Information Engineering, Hohai University, Nanjing, 211100, China

    Hong-min Gao  (高红民), Hui Zhou  (周惠), Li-zhong Xu  (徐立中) & Ai-ye Shi  (石爱业)

  2. College of Computer and Software, Nanjing Institute of Industry Technology, Nanjing, 210046, China

    Hui Zhou  (周惠)

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  1. Hong-min Gao  (高红民)
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  2. Hui Zhou  (周惠)
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  3. Li-zhong Xu  (徐立中)
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  4. Ai-ye Shi  (石爱业)
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Correspondence to Li-zhong Xu  (徐立中).

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Gao, Hm., Zhou, H., Xu, Lz. et al. Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning. J. Cent. South Univ. 21, 262–271 (2014). https://doi.org/10.1007/s11771-014-1937-0

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  • DOI: https://doi.org/10.1007/s11771-014-1937-0

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