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Proceedings Paper

Improved extreme learning machine and its application in SAR target recognition
Author(s): Jian Chen
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Paper Abstract

In this paper, An improved algorithm for the extreme learning machine is proposed and applied to SAR target recognition.In order to solve the influence of the noise and spatial distribution of the training samples on the calculation of the classification plane, different penalty factors are given to different training samples, and according to this, the “weighted extreme learning machine” is proposed. And then,the kernel function is introduced into the "extreme learning machine" to improve the ability of nonlinear function approximation. Considering that the general training algorithm of the weighted extreme learning machine is slow and consumes a lot of computer memory when the number of training samples is large, a training method based on conjugate gradient algorithm is proposed. The test on "banana benchmark data" shows that the weighted extreme learning machine based on the conjugate gradient method can complete the convergence in the number of iterations far less than the number of samples, and the calculation speed is much faster than the traditional algorithm. Finally, this proposed algorithm is applied to SAR target recognition. The test on MSTAR data set shows that the proposed algorithm is not only extremely fast in SAR target recognition, but also has better recognition performance than support vector machine, general limit learning machine, BP neural network and other algorithms.

Paper Details

Date Published: 27 November 2019
PDF: 9 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 1132124 (27 November 2019); doi: 10.1117/12.2543665
Show Author Affiliations
Jian Chen, Fuzhou Univ. (China)

Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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