Schwegmann, Colin PKleynhans, WaldoSalmon, BPMdakane, Lizwe WMeyer, Rory GVJanoth, JLumsdon, P2019-04-102019-04-102018-07Schwegmann, Colin, P., Kleynhans, W., Salmon, B.P. Mdakane, L.W. Meyer, R.G.V. Janoth, J. & Lumsdon, P .2018. Transfer learning for multi-frequency synthetic aperture radar applications. In: 2018 IGARSS: International Geoscience and Remote Sensing Symposium, 22-27 July 2018, Valencia, Spain978-1-5386-7150-4978-1-5386-7149-8https://ieeexplore.ieee.org/document/8518401DOI: 10.1109/IGARSS.2018.8518401https://www.igarss2018.org/Papers/viewpapers.asp?papernum=3926http://hdl.handle.net/10204/10939Presented in: 2018 IGARSS: International Geoscience and Remote Sensing Symposium, 22-27 July 2018, Valencia, Spain. Due to copyright restrictions, the attached PDF file only contains the abstract of the full-text item. For access to the full-text item, please consult the publisher's website. While waiting for the post-print or published PDF document from the publisherTo correctly train a system to detect ships in Sythetic Aperture Radar imagery requires the creation of a validated dataset. This dataset is used to train a Machine Learning system to identify ships and non-ships. Recent advances in Deep Learning have focused on Transfer Learning which uses characteristics from one dataset to classify another without retraining the system. In this work two Deep Learning architectures were trained on lower resolution C-band SAR ship data and then tested against an unseen set of higher resolution Xband SAR ship data. Transfer Learning allowed the system to correctly identify 81% of the ships and 93% of the unseen data. This represents a possible reduction in the amount of effort required to curate new SAR datasets in the future by pre-identifying likely candidates as ships or non-ships.enMachine learningMarine technologySyntetic aperture radarConference PresentationSchwegmann, C. P., Kleynhans, W., Salmon, B., Mdakane, L. W., Meyer, R. G., Janoth, J., & Lumsdon, P. (2018). Transfer learning for multi-frequency synthetic aperture radar applications. IEEE. http://hdl.handle.net/10204/10939Schwegmann, Colin P, Waldo Kleynhans, BP Salmon, Lizwe W Mdakane, Rory GV Meyer, J Janoth, and P Lumsdon. "Transfer learning for multi-frequency synthetic aperture radar applications." (2018): http://hdl.handle.net/10204/10939Schwegmann CP, Kleynhans W, Salmon B, Mdakane LW, Meyer RG, Janoth J, et al, Transfer learning for multi-frequency synthetic aperture radar applications; IEEE; 2018. http://hdl.handle.net/10204/10939 .TY - Conference Presentation AU - Schwegmann, Colin P AU - Kleynhans, Waldo AU - Salmon, BP AU - Mdakane, Lizwe W AU - Meyer, Rory GV AU - Janoth, J AU - Lumsdon, P AB - To correctly train a system to detect ships in Sythetic Aperture Radar imagery requires the creation of a validated dataset. This dataset is used to train a Machine Learning system to identify ships and non-ships. Recent advances in Deep Learning have focused on Transfer Learning which uses characteristics from one dataset to classify another without retraining the system. In this work two Deep Learning architectures were trained on lower resolution C-band SAR ship data and then tested against an unseen set of higher resolution Xband SAR ship data. Transfer Learning allowed the system to correctly identify 81% of the ships and 93% of the unseen data. This represents a possible reduction in the amount of effort required to curate new SAR datasets in the future by pre-identifying likely candidates as ships or non-ships. DA - 2018-07 DB - ResearchSpace DP - CSIR KW - Machine learning KW - Marine technology KW - Syntetic aperture radar LK - https://researchspace.csir.co.za PY - 2018 SM - 978-1-5386-7150-4 SM - 978-1-5386-7149-8 T1 - Transfer learning for multi-frequency synthetic aperture radar applications TI - Transfer learning for multi-frequency synthetic aperture radar applications UR - http://hdl.handle.net/10204/10939 ER -