Meyer, Rory GVSchwegmann, Colin PKleynhans, Waldo2018-05-312018-05-312017-07Meyer, R.G.V., Schwegmann, C.P. and Kleynhans, W. 2017. Vessel classification features using spatial Bayesian inference from historical ais data. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 23-28 July 2017, Fort Worth, Texas, USAMeyer,http://ieeexplore.ieee.org/document/8127534/http://hdl.handle.net/10204/10247Copyright: 2017 IEEE. 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.Detections and classification of non-AIS-compliant vessels is an important ability for countries or institutions interested in MDA. SAR has been proven to be an effective method but there exists a trade-off between the area that can be imaged and the resolution of each image pixel. Large swath SAR images are a cost effective method of performing maritime surveillance but classification or identification from the images remains a challenge. An algorithm to predict the AIS class of a vessel using historical AIS data and SAR derived features is described in this paper. The novel algorithm calculates the class probability by taking historical AIS data into account using a Bayesian algorithm. Features extracted from SAR imagery are then used with the AIS historical data to provide a list of class probabilities that can enhance other course resolution classification algorithms or to flag vessels that do not conform to historical class behaviour.enBayesian statisticsSpatial dataAutomated Identification SystemAISAIS dataVessel classification featuresConference PresentationMeyer, R. G., Schwegmann, C. P., & Kleynhans, W. (2017). Vessel classification features using spatial Bayesian inference from historical AIS data. IEEE. http://hdl.handle.net/10204/10247Meyer, Rory GV, Colin P Schwegmann, and Waldo Kleynhans. "Vessel classification features using spatial Bayesian inference from historical AIS data." (2017): http://hdl.handle.net/10204/10247Meyer RG, Schwegmann CP, Kleynhans W, Vessel classification features using spatial Bayesian inference from historical AIS data; IEEE; 2017. http://hdl.handle.net/10204/10247 .TY - Conference Presentation AU - Meyer, Rory GV AU - Schwegmann, Colin P AU - Kleynhans, Waldo AB - Detections and classification of non-AIS-compliant vessels is an important ability for countries or institutions interested in MDA. SAR has been proven to be an effective method but there exists a trade-off between the area that can be imaged and the resolution of each image pixel. Large swath SAR images are a cost effective method of performing maritime surveillance but classification or identification from the images remains a challenge. An algorithm to predict the AIS class of a vessel using historical AIS data and SAR derived features is described in this paper. The novel algorithm calculates the class probability by taking historical AIS data into account using a Bayesian algorithm. Features extracted from SAR imagery are then used with the AIS historical data to provide a list of class probabilities that can enhance other course resolution classification algorithms or to flag vessels that do not conform to historical class behaviour. DA - 2017-07 DB - ResearchSpace DP - CSIR KW - Bayesian statistics KW - Spatial data KW - Automated Identification System KW - AIS KW - AIS data KW - Vessel classification features LK - https://researchspace.csir.co.za PY - 2017 SM - Meyer, SM - http://ieeexplore.ieee.org/document/8127534/ T1 - Vessel classification features using spatial Bayesian inference from historical AIS data TI - Vessel classification features using spatial Bayesian inference from historical AIS data UR - http://hdl.handle.net/10204/10247 ER -