Fusing deep learning and sparse coding for SAR ATR
| dc.contributor.author | Kechagias-Stamatis, Odysseas | |
| dc.contributor.author | Aouf, Nabil | |
| dc.date.accessioned | 2018-10-19T10:45:18Z | |
| dc.date.available | 2018-10-19T10:45:18Z | |
| dc.date.issued | 2019-04 | |
| dc.date.pubOnline | 2018-08-10 | |
| dc.description.abstract | We 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.description.journalName | IEEE Transactions on Aerospace and Electronic Systems | |
| dc.format.extent | 785-797 | |
| dc.identifier.citation | Kechagias-Stamatis O, Aouf N. (2019) Fusing deep learning and sparse coding for SAR ATR. IEEE Transactions on Aerospace and Electronic Systems, Volume 55, Issue 2, April 2018, pp. 785-797 | en_UK |
| dc.identifier.issn | 0018-9251 | |
| dc.identifier.issueNo | 2 | |
| dc.identifier.uri | https://doi.org/10.1109/TAES.2018.2864809 | |
| dc.identifier.uri | http://dspace.lib.cranfield.ac.uk/handle/1826/13548 | |
| dc.identifier.volumeNo | 55 | |
| 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 | Automatic Target Recognition | en_UK |
| dc.subject | Convolutional Neural Networks | en_UK |
| dc.subject | Data Fusion | en_UK |
| dc.subject | Sparse Coding | en_UK |
| dc.subject | Synthetic Aperture Radar | en_UK |
| dc.title | Fusing deep learning and sparse coding for SAR ATR | en_UK |
| dc.type | Article | en_UK |
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