dc.contributor.author |
Meyer, Rory GV
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dc.contributor.author |
Schwegmann, Colin P
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dc.contributor.author |
Kleynhans, Waldo
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dc.date.accessioned |
2018-05-31T11:42:30Z |
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dc.date.available |
2018-05-31T11:42:30Z |
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dc.date.issued |
2017-07 |
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dc.identifier.citation |
Meyer, 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, USA |
en_US |
dc.identifier.isbn |
Meyer, |
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dc.identifier.issn |
http://ieeexplore.ieee.org/document/8127534/ |
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dc.identifier.uri |
http://hdl.handle.net/10204/10247
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dc.description |
Copyright: 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. |
en_US |
dc.description.abstract |
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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Workflow;20107 |
|
dc.subject |
Bayesian statistics |
en_US |
dc.subject |
Spatial data |
en_US |
dc.subject |
Automated Identification System |
en_US |
dc.subject |
AIS |
en_US |
dc.subject |
AIS data |
en_US |
dc.subject |
Vessel classification features |
en_US |
dc.title |
Vessel classification features using spatial Bayesian inference from historical AIS data |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Meyer, R. G., Schwegmann, C. P., & Kleynhans, W. (2017). Vessel classification features using spatial Bayesian inference from historical AIS data. IEEE. http://hdl.handle.net/10204/10247 |
en_ZA |
dc.identifier.chicagocitation |
Meyer, Rory GV, Colin P Schwegmann, and Waldo Kleynhans. "Vessel classification features using spatial Bayesian inference from historical AIS data." (2017): http://hdl.handle.net/10204/10247 |
en_ZA |
dc.identifier.vancouvercitation |
Meyer RG, Schwegmann CP, Kleynhans W, Vessel classification features using spatial Bayesian inference from historical AIS data; IEEE; 2017. http://hdl.handle.net/10204/10247 . |
en_ZA |
dc.identifier.ris |
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 -
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en_ZA |