Kechagias-Stamatis, OdysseasAouf, NabilBelloni, Carole D. L.2019-05-022019-05-022018-05-28O 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 2017978-1-78561-673-010.1049/cp.2017.0437https://ieeexplore.ieee.org/document/8367522http://dspace.lib.cranfield.ac.uk/handle/1826/14127We 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.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Automatic Target recognitionConvolutional Neural NetworksDeep LearningSupport Vector MachineSynthetic Aperture RadarConference paper