Fusing deep learning and sparse coding for SAR ATR

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dc.contributor.authorKechagias-Stamatis, Odysseas
dc.contributor.authorAouf, Nabil
dc.date.accessioned2018-10-19T10:45:18Z
dc.date.available2018-10-19T10:45:18Z
dc.date.issued2018-08-10
dc.description.abstractWe propose a multi-modal and multi-discipline data fusion strategy appropriate for Automatic Target Recognition (ATR) on Synthetic Aperture Radar imagery. Our architecture fuses a proposed Clustered version of the AlexNet Convolutional Neural Network with Sparse Coding theory that is extended to facilitate an adaptive elastic net optimization concept. Evaluation on the MSTAR dataset yields the highest ATR performance reported yet which is 99.33% and 99.86% for the 3 and 10-class problems respectively.en_UK
dc.identifier.citationOdysseas Kechagias-Stamatis and Nabil Aouf. Fusing deep learning and sparse coding for SAR ATR. IEEE Transactions on Aerospace and Electronic Systems, Volume 55, Issue 2, 2018, pp. 785-797en_UK
dc.identifier.issn0018-9251
dc.identifier.urihttps://doi.org/10.1109/TAES.2018.2864809
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/13548
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectAutomatic Target Recognitionen_UK
dc.subjectConvolutional Neural Networksen_UK
dc.subjectData Fusionen_UK
dc.subjectSparse Codingen_UK
dc.subjectSynthetic Aperture Radaren_UK
dc.titleFusing deep learning and sparse coding for SAR ATRen_UK
dc.typeArticleen_UK

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