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Fusing Deep Learning and Sparse Coding for SAR ATR

Kechagias-Stamatis, O. and Aouf, N. ORCID: 0000-0001-9291-4077 (2019). Fusing Deep Learning and Sparse Coding for SAR ATR. IEEE Transactions on Aerospace and Electronic Systems, 55(2), pp. 785-797. doi: 10.1109/TAES.2018.2864809

Abstract

We propose a multimodal and multidiscipline 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 three- and ten-class problems, respectively.

Publication Type: Article
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Synthetic aperture radar, Optimization, Training, Dictionaries, Fuses, Two dimensional displays, Encoding
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
U Military Science
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Mechanical Engineering & Aeronautics
URI: https://openaccess.city.ac.uk/id/eprint/23175
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