dc.contributor.author | Belloni, Carole | |
dc.contributor.author | Aouf, Nabil | |
dc.contributor.author | Balleri, Alessio | |
dc.contributor.author | Le Caillec, Jean-Marc | |
dc.contributor.author | Merlet, Thomas | |
dc.date.accessioned | 2020-11-20T10:51:23Z | |
dc.date.available | 2020-11-20T10:51:23Z | |
dc.date.issued | 2020-10-20 | |
dc.identifier.citation | Belloni 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 - 673 | en_UK |
dc.identifier.issn | 0018-9251 | |
dc.identifier.uri | https://doi.org/10.1109/TAES.2020.3031435 | |
dc.identifier.uri | http://dspace.lib.cranfield.ac.uk/handle/1826/16021 | |
dc.description.abstract | Understanding 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.language.iso | en | en_UK |
dc.publisher | IEEE | en_UK |
dc.rights | Attribution-NonCommercial 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
dc.subject | Deep Learning | en_UK |
dc.subject | SAR | en_UK |
dc.subject | ATR | en_UK |
dc.subject | Explainability | en_UK |
dc.subject | Features | en_UK |
dc.title | Explainability of deep SAR ATR through feature analysis | en_UK |
dc.type | Article | en_UK |
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