A master thesis from Aalborg University
Ship and Oil Spill Detection using Convolutional Autoencoder
Author(s)
Term
4. term
Publication year
2022
Submitted on
2022-06-02
Abstract
Maritime surveillance has seen a growing interest in past few years owing to the fact that marine traffic has increased quite a lot, and securing a nation's maritime boundary is a crucial task. In this report we focus on two aspects of the maritime surveillance, namely ship detection and oil spill detection in SAR (Synthetic Aperture RADAR) satellite imagery. In this report the problem of ship and oil spill detection is formulated as that of an anomaly detection, where the ships and oil spills are the anomalies in the otherwise ocean background and use a deep Convolutional Autoencoder for the purpose of detecting these anomalies. Anomaly dataset was created from the xView3 competition dataset and SOS oil spill dataset for the training and testing of the autoencoder. The overall accuracy of the model is 96.64\%, and the precision, recall and F1 score being 93.32\%, 98.80\% and 97.96\%, respectively.
Documents
Colophon: This page is part of the AAU Student Projects portal, which is run by Aalborg University. Here, you can find and download publicly available bachelor's theses and master's projects from across the university dating from 2008 onwards. Student projects from before 2008 are available in printed form at Aalborg University Library.
If you have any questions about AAU Student Projects or the research registration, dissemination and analysis at Aalborg University, please feel free to contact the VBN team. You can also find more information in the AAU Student Projects FAQs.