Fusing deep learning and sparse coding for SAR ATR
Date
2018-08-10
Supervisor/s
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Department
Type
Article
ISSN
0018-9251
Format
Citation
Odysseas 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. 785-797
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.
Description
Software Description
Software Language
Github
Keywords
Automatic Target Recognition, Convolutional Neural Networks, Data Fusion, Sparse Coding, Synthetic Aperture Radar
DOI
Rights
Attribution-NonCommercial 4.0 International