Exploring the capabilities of deep learning in seasurveillance : Using deep learning to classify motion trajectories from AIS data
Abstract: In this master thesis deep learning is proven to be applicable in the field of seasurveillance. Commercial ships using the AIS system have to report the type of thevessel such as fishing ship or cargo ship. A problem with AIS data is that it can beeasily manipulated and therefore deliberately or accidentally incorrect. This thesis will focus on detecting false ship types. To detect a false ship type 19 different methods were tested on the 1100 hour long AIS data set. Three of these methods were baseline methods using a more conventional approach to the sea surveillanceproblem. The testing showed that the best performing method was one of the deeplearning methods proving that deep learning is indeed suitable in sea surveillance.
AT THIS PAGE YOU CAN DOWNLOAD THE WHOLE ESSAY. (follow the link to the next page)