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The random boosting ensemble classifier for land-use image classification

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Abstract

This paper presents a random boosting ensemble (RBE) classifier for remote sensing image classification, which introduces the random projection feature selection and bootstrap methods to obtain base classifiers for classifier ensemble. The RBE method is built based on an improved boosting framework, which is quite efficient for the few-shot problem due to the bootstrap in use. In RBE, kernel extreme machine (KELM) is applied to design base classifiers, which actually make RBE quite efficient due to feature reduction. The experimental results on the remote scene image classification demonstrate that RBE can effectively improve the classification performance, and resulting into a better generalization ability on the 21-class land-use dataset and the India pine satellite scene dataset.

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Acknowledgements

Thanks for Linlin Yang for his help on the idea and source code, thanks for Prof. Jun Miao and Baochang Zhang for the help on the revision on the paper.

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, No. 37, Xueyuan Road, Haidian District, Beijing, 100083, China

    Hainan Wang & Yunqi Miao

  2. School of Mechanical Engineering, Guizhou University, Huaxi District, Guiyang, 550025, Guizhou, China

    Hainan Wang

Authors
  1. Hainan Wang
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  2. Yunqi Miao
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Correspondence to Hainan Wang.

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Wang, H., Miao, Y. The random boosting ensemble classifier for land-use image classification. Multimed Tools Appl 77, 29933–29947 (2018). https://doi.org/10.1007/s11042-018-6085-3

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  • DOI: https://doi.org/10.1007/s11042-018-6085-3

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