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An efficient radial basis function neural network for hyperspectral remote sensing image classification

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Abstract

A very simple radial basis function neural network (RBFNN) is investigated for hyperspectral remote sensing image classification. Its training can be analytically solved with a closed-form equation, and no parameter needs to be manually tuned. Its computational cost is much lower than the popular support vector machine (SVM). Surprisingly, such an RBFNN can achieve the performance that is similar to or even better than the SVM. By incorporating a simple spatial averaging filter or a Gaussian lowpass filter with negligible additional computational cost, classification accuracy can be further improved. Considering the large matrix inversion operation in the RBFNN when the number of training samples being very large, we also propose a parallel processing method to reduce computing time in matrix inversion.

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Authors and Affiliations

  1. School of Telecommunication, Xidian University, Xi’an, China

    Jiaojiao Li & Yunsong Li

  2. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, USA

    Qian Du

Authors
  1. Jiaojiao Li
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  2. Qian Du
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  3. Yunsong Li
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Corresponding author

Correspondence to Qian Du.

Additional information

Communicated by Y.-S. Ong.

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Li, J., Du, Q. & Li, Y. An efficient radial basis function neural network for hyperspectral remote sensing image classification. Soft Comput 20, 4753–4759 (2016). https://doi.org/10.1007/s00500-015-1739-9

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  • DOI: https://doi.org/10.1007/s00500-015-1739-9

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