eCite Digital Repository
Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks
Citation
Schwegmann, CP and Kleynhans, W and Salmon, BP and Mdakane, LW and Meyer, RGV, Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks, Proceedings from the 2017 IEEE International Geoscience and Remote Sensing Symposium, 23-28 July 2017, Texas, United State, pp. 1-4. ISBN 9781509049516 (2017) [Refereed Conference Paper]
![]() | PDF 655Kb |
Copyright Statement
Copyright 2017 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.
Official URL: http://dx.doi.org/10.1109/IGARSS.2017.8127440
Abstract
Item Details
Item Type: | Refereed Conference Paper |
---|---|
Keywords: | synthetic aperture radar, machine learning, marine technology |
Research Division: | Engineering |
Research Group: | Communications engineering |
Research Field: | Signal processing |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in engineering |
UTAS Author: | Salmon, BP (Dr Brian Salmon) |
ID Code: | 124430 |
Year Published: | 2017 |
Deposited By: | Engineering |
Deposited On: | 2018-02-21 |
Last Modified: | 2018-06-18 |
Downloads: | 103 View Download Statistics |
Repository Staff Only: item control page