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The development of deep learning in synthetic aperture radar imagery


Schwegmann, CP and Kleynhans, W and Salmon, BP, The development of deep learning in synthetic aperture radar imagery, 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), 18-21 May 2017, Shanghai, China, pp. 5-6. ISBN 978-1-5386-1990-2 (2017) [Refereed Conference Paper]

Copyright Statement

Copyright 2017 IEEE

DOI: doi:10.1109/RSIP.2017.7958802


The usage of remote sensing to observe environments necessitates interdisciplinary approaches to derive effective, impactful research. One remote sensing technique, Synthetic Aperture Radar, has shown significant benefits over traditional remote sensing techniques but comes at the price of additional complexities. To adequately cope with these, researchers have begun to employ advanced machine learning techniques known as deep learning to Synthetic Aperture Radar data. Deep learning represents the next stage in the evolution of machine intelligence which places the onus of identifying salient features on the network rather than researcher. This paper will outline machine learning techniques as it has been used previously on SAR; what is deep learning and where it fits in compared to traditional machine learning; what benefits can be derived by applying it to Synthetic Aperture Radar imagery; and finally describe some obstacles that still need to be overcome in order to provide constient and long term results from deep learning in SAR.

Item Details

Item Type:Refereed Conference Paper
Keywords:synthetic aperture radar, machine learning, marine technology, monitoring
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:119330
Year Published:2017
Deposited By:Engineering
Deposited On:2017-07-31
Last Modified:2018-06-18

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