A novel separability objective function in CNN for feature extraction of SAR images

Tools

Gao, Fei and Wang, Meng and Wang, Jun and Yang, Erfu and Zhou, Huiyu (2019) A novel separability objective function in CNN for feature extraction of SAR images. Chinese Journal of Electronics, 28 (2). pp. 423-429. ISSN 1022-4653 (https://doi.org/10.1049/cje.2018.12.001)

[thumbnail of Gao-etal-CJE-2019-A-novel-separability-objective-function-in-CNN]
Preview
 Text. Filename: Gao_etal_CJE_2019_A_novel_separability_objective_function_in_CNN.pdf
Accepted Author Manuscript
Download (828kB)| Preview

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.

ORCID iDs

Gao, Fei, Wang, Meng, Wang, Jun, Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950 and Zhou, Huiyu;
CORE (COnnecting REpositories)