Abstract
Convolutional neural network (CNN) has become a promising method for Synthetic aperture radar (SAR) target recognition. Existing CNN models aim at seeking the best separation between classes, but rarely care about the separability of them. We performs a separability measure by analyzing the property of linear separability, and proposes an objective function for CNN to extract linearly separable features. The experimental results indicate the output features are linearly separable, and the classification results are comparable with the other state of the art techniques.
Original language | English |
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Pages (from-to) | 423-429 |
Number of pages | 7 |
Journal | Chinese Journal of Electronics |
Volume | 28 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Mar 2019 |
Keywords
- classification
- convolution neural network (CNN)
- linear separability
- objective function
- synthetic aperture radar (SAR)