Explainability of deep SAR ATR through feature analysis

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dc.contributor.authorBelloni, Carole
dc.contributor.authorAouf, Nabil
dc.contributor.authorBalleri, Alessio
dc.contributor.authorLe Caillec, Jean-Marc
dc.contributor.authorMerlet, Thomas
dc.date.accessioned2020-11-20T10:51:23Z
dc.date.available2020-11-20T10:51:23Z
dc.date.issued2020-10-20
dc.description.abstractUnderstanding the decision-making process of deep learning networks is a key challenge which has rarely been investigated for Synthetic Aperture Radar (SAR) images. In this paper, a set of new analytical tools is proposed and applied to a Convolutional Neural Network (CNN) handling Automatic Target Recognition (ATR) on two SAR datasets containing military targets.en_UK
dc.identifier.citationBelloni C, Aouf N, Balleri A, et al., (2020) Explainability of deep SAR ATR through feature analysis. IEEE Transactions on Aerospace and Electronic Systems, Volume 57, Issue 1, February 2021, pp. 659 - 673en_UK
dc.identifier.issn0018-9251
dc.identifier.urihttps://doi.org/10.1109/TAES.2020.3031435
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16021
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDeep Learningen_UK
dc.subjectSARen_UK
dc.subjectATRen_UK
dc.subjectExplainabilityen_UK
dc.subjectFeaturesen_UK
dc.titleExplainability of deep SAR ATR through feature analysisen_UK
dc.typeArticleen_UK

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