Schwegmann, Colin PKleynhans, WaldoSalmon, BPMdakane, Lizwe WMeyer, Rory GV2019-04-102019-04-102018-07Schwegmann, C.P., Kleynhans, W., Salmon, B.P., Mdakane, L.W. & Meyer, R.G.V. 2018. Synthetic aperture radar ship detection using capsule networks. In: 2018 IGARSS: International Geoscience and Remote Sensing Symposium, 22-27 July 2018, Valencia, Spain978-1-5386-7150-4978-1-5386-7149-8https://ig18.s3.amazonaws.com/IG18_ProgramGuide_web3.pdfhttps://ieeexplore.ieee.org/abstract/document/8517804http://hdl.handle.net/10204/10946Presented 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 publisherDeep Learning has become a common applied technique to improve results in a variety of detection scenarios, including Synthetic Aperture Radar ship detection. One of the most common Deep Learning techniques, Convolutional Neural Networks, have provided excellent results but have a number of limitations such as pose/position invariance. If the position of an object relative to other objects is important these details may be lost by CNN. To combat this a new Deep Learning architecture known as Capsule Networks have been introduced. Capsule networks encode various object parameters in addition to feature values to attempt to improve upon convolutions. This paper introduces new ship detection technique based on Capsule Networks and was tested against two other machine learning techniques. The new architecture shows improved ship detection accuracy of 91.03% and false alarm rates of 9:5745 x 10(sup-9) and includes additional benefits such as requiring fewer samples/iterations to achieve improved performance.enSynthetic aperture radarMachine learningMarine technologyConference PresentationSchwegmann, C. P., Kleynhans, W., Salmon, B., Mdakane, L. W., & Meyer, R. G. (2018). Synthetic aperture radar ship detection using capsule networks. IEEE. http://hdl.handle.net/10204/10946Schwegmann, Colin P, Waldo Kleynhans, BP Salmon, Lizwe W Mdakane, and Rory GV Meyer. "Synthetic aperture radar ship detection using capsule networks." (2018): http://hdl.handle.net/10204/10946Schwegmann CP, Kleynhans W, Salmon B, Mdakane LW, Meyer RG, Synthetic aperture radar ship detection using capsule networks; IEEE; 2018. http://hdl.handle.net/10204/10946 .TY - Conference Presentation AU - Schwegmann, Colin P AU - Kleynhans, Waldo AU - Salmon, BP AU - Mdakane, Lizwe W AU - Meyer, Rory GV AB - Deep Learning has become a common applied technique to improve results in a variety of detection scenarios, including Synthetic Aperture Radar ship detection. One of the most common Deep Learning techniques, Convolutional Neural Networks, have provided excellent results but have a number of limitations such as pose/position invariance. If the position of an object relative to other objects is important these details may be lost by CNN. To combat this a new Deep Learning architecture known as Capsule Networks have been introduced. Capsule networks encode various object parameters in addition to feature values to attempt to improve upon convolutions. This paper introduces new ship detection technique based on Capsule Networks and was tested against two other machine learning techniques. The new architecture shows improved ship detection accuracy of 91.03% and false alarm rates of 9:5745 x 10(sup-9) and includes additional benefits such as requiring fewer samples/iterations to achieve improved performance. DA - 2018-07 DB - ResearchSpace DP - CSIR KW - Synthetic aperture radar KW - Machine learning KW - Marine technology LK - https://researchspace.csir.co.za PY - 2018 SM - 978-1-5386-7150-4 SM - 978-1-5386-7149-8 T1 - Synthetic aperture radar ship detection using capsule networks TI - Synthetic aperture radar ship detection using capsule networks UR - http://hdl.handle.net/10204/10946 ER -