Schwegmann, Colin PKleynhans, WaldoSalmon, BPMdakane, Lizwe WMeyer, Rory GV2017-05-172017-05-172016-07Schwegmann, C.P., Kleynhans, W., Salmon, B.P., Mdakane, L.W. and Meyer, R.G.V. 2016. Very deep learning for ship discrimination in synthetic aperture radar imagery. International Geoscience and Remote Sensing Symposium (IEEE IGARSS), 10-15 July 2016, Beijing, China. DOI: 10.1109/IGARSS.2016.77308002153-7003http://ieeexplore.ieee.org/document/7729017/DOI: 10.1109/IGARSS.2016.7729017http://hdl.handle.net/10204/9083International Geoscience and Remote Sensing Symposium (IEEE IGARSS), 10-15 Juy 2016, Beijing, China. 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.Efficient and effective ship discrimination across multiple Synthetic Aperture Radar sensors is becoming more important as access to SAR data becomes more widespread. A flexible means of separating ships from sea is ideal and can be accomplished using machine learning. Newer, advanced deep learning techniques offer a unique solution but traditionally require a large dataset to train effectively. Highway Networks allow for very deep networks that can be trained using the smaller datasets typical in SAR-based ship detection. A flexible network configuration is possible within Highway Networks due to an adaptive gating mechanism which prevents gradient decay across many layers. This paper presents a very deep High Network configuration as a ship discrimination stage for SAR ship detection. It also presents a three-class SAR dataset that allows for more meaningful analysis of ship discrimination performances. The proposed method was tested on a this SAR dataset and had the highest mean accuracy of all methods tested at 96:67%. The proposed ship discrimination method also provides improved false positive classification compared to the other methods tested.enSynthetic aperture radarMachine learningMarine technologyConference PresentationSchwegmann, C. P., Kleynhans, W., Salmon, B., Mdakane, L. W., & Meyer, R. G. (2016). Very deep learning for ship discrimination in synthetic aperture radar imagery. IEEE. http://hdl.handle.net/10204/9083Schwegmann, Colin P, Waldo Kleynhans, BP Salmon, Lizwe W Mdakane, and Rory GV Meyer. "Very deep learning for ship discrimination in synthetic aperture radar imagery." (2016): http://hdl.handle.net/10204/9083Schwegmann CP, Kleynhans W, Salmon B, Mdakane LW, Meyer RG, Very deep learning for ship discrimination in synthetic aperture radar imagery; IEEE; 2016. http://hdl.handle.net/10204/9083 .TY - Conference Presentation AU - Schwegmann, Colin P AU - Kleynhans, Waldo AU - Salmon, BP AU - Mdakane, Lizwe W AU - Meyer, Rory GV AB - Efficient and effective ship discrimination across multiple Synthetic Aperture Radar sensors is becoming more important as access to SAR data becomes more widespread. A flexible means of separating ships from sea is ideal and can be accomplished using machine learning. Newer, advanced deep learning techniques offer a unique solution but traditionally require a large dataset to train effectively. Highway Networks allow for very deep networks that can be trained using the smaller datasets typical in SAR-based ship detection. A flexible network configuration is possible within Highway Networks due to an adaptive gating mechanism which prevents gradient decay across many layers. This paper presents a very deep High Network configuration as a ship discrimination stage for SAR ship detection. It also presents a three-class SAR dataset that allows for more meaningful analysis of ship discrimination performances. The proposed method was tested on a this SAR dataset and had the highest mean accuracy of all methods tested at 96:67%. The proposed ship discrimination method also provides improved false positive classification compared to the other methods tested. DA - 2016-07 DB - ResearchSpace DP - CSIR KW - Synthetic aperture radar KW - Machine learning KW - Marine technology LK - https://researchspace.csir.co.za PY - 2016 SM - 2153-7003 T1 - Very deep learning for ship discrimination in synthetic aperture radar imagery TI - Very deep learning for ship discrimination in synthetic aperture radar imagery UR - http://hdl.handle.net/10204/9083 ER -