SAR automatic target recognition based on convolutional neural networks

Show simple item record Kechagias-Stamatis, Odysseas Aouf, Nabil Belloni, Carole D. L. 2019-05-02T18:52:09Z 2019-05-02T18:52:09Z 2018-05-28
dc.identifier.citation O Kechagias-Stamatis, N Aouf and CDL Belloni. SAR automatic target recognition based on convolutional neural networks. In: IET International Conference on Radar Systems (Radar 2017), Belfast, 23-26 October 2017 en_UK
dc.identifier.isbn 978-1-78561-673-0
dc.identifier.uri 10.1049/cp.2017.0437
dc.description.abstract We propose a multi-modal multi-discipline strategy appropriate for Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) imagery. Our architecture relies on a pre-trained, in the RGB domain, Convolutional Neural Network that is innovatively applied on SAR imagery, and is combined with multiclass Support Vector Machine classification. The multi-modal aspect of our architecture enforces the generalisation capabilities of our proposal, while the multi-discipline aspect bridges the modality gap. Even though our technique is trained in a single depression angle of 17°, average performance on the MSTAR database over a 10-class target classification problem in 15°, 30° and 45° depression is 97.8%. This multi-target and multi-depression ATR capability has not been reported yet in the MSTAR database literature. en_UK
dc.language.iso en en_UK
dc.publisher IEEE en_UK
dc.rights Attribution-NonCommercial 4.0 International *
dc.rights.uri *
dc.subject Automatic Target recognition en_UK
dc.subject Convolutional Neural Networks en_UK
dc.subject Deep Learning en_UK
dc.subject Support Vector Machine en_UK
dc.subject Synthetic Aperture Radar en_UK
dc.title SAR automatic target recognition based on convolutional neural networks en_UK
dc.type Conference paper en_UK

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