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Hyperspectral image classification based on multiple reduced kernel extreme learning machine

  • Fei LvEmail author
  • Min Han
Original Article
  • 13 Downloads

Abstract

This paper presents an efficient hyperspectral images classification method based on multiple reduced kernel extreme learning machine (MRKELM). The MRKELM model is developed on the basis of the multiple kernel leaning method and the reduced kernel extreme learning machine method. In the presented MRKELM, the kernel function are not fixed anymore, multiple kernels are adaptively trained as a hybrid kernel and the optimal kernel combination weights are jointly optimized. Finally, two simulation examples, classification of benchmark datasets and classification of hyperspectral images including Indian Pines, University of Pavia, and Salinas respectively, are used testify the performance of the proposed MRKELM method.

Keywords

Classification Hyperspectral image Reduced kernel extreme learning machine Multiple reduced kernel extreme learning machine 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant no. 61374154 and the National Basic Research Program of China under Grant no. 2013CB430403.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

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