Deep Learning for Iceberg Detection in Satellite Images
The application of satellite images for ship and iceberg monitoring is essential in many ways in Arctic waters. Even though the detection of ships and icebergs in images is well established using Geoscience techniques, the discrimination between those two target classes still represents a challenge...
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Uppsala universitet, Institutionen för informationsteknologi
2021
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ftuppsalauniv:oai:DiVA.org:uu-436032 2023-05-15T15:00:12+02:00 Deep Learning for Iceberg Detection in Satellite Images Dong, Shuzhi 2021 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-436032 eng eng Uppsala universitet, Institutionen för informationsteknologi IT 21010 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-436032 info:eu-repo/semantics/openAccess Engineering and Technology Teknik och teknologier Student thesis info:eu-repo/semantics/bachelorThesis text 2021 ftuppsalauniv 2023-02-23T21:54:59Z The application of satellite images for ship and iceberg monitoring is essential in many ways in Arctic waters. Even though the detection of ships and icebergs in images is well established using Geoscience techniques, the discrimination between those two target classes still represents a challenge for operational scenarios. This thesis project proposes the application of Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and SingleShot Detector (SSD) for ship-iceberg detection in satellite images. The CNN model is compared with SVM and SSD, and the final results indicate not only a superior classification performance of the proposed methods but also the object detection results from SSD. Bachelor Thesis Arctic Iceberg* Uppsala University: Publications (DiVA) Arctic |
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Open Polar |
collection |
Uppsala University: Publications (DiVA) |
op_collection_id |
ftuppsalauniv |
language |
English |
topic |
Engineering and Technology Teknik och teknologier |
spellingShingle |
Engineering and Technology Teknik och teknologier Dong, Shuzhi Deep Learning for Iceberg Detection in Satellite Images |
topic_facet |
Engineering and Technology Teknik och teknologier |
description |
The application of satellite images for ship and iceberg monitoring is essential in many ways in Arctic waters. Even though the detection of ships and icebergs in images is well established using Geoscience techniques, the discrimination between those two target classes still represents a challenge for operational scenarios. This thesis project proposes the application of Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and SingleShot Detector (SSD) for ship-iceberg detection in satellite images. The CNN model is compared with SVM and SSD, and the final results indicate not only a superior classification performance of the proposed methods but also the object detection results from SSD. |
format |
Bachelor Thesis |
author |
Dong, Shuzhi |
author_facet |
Dong, Shuzhi |
author_sort |
Dong, Shuzhi |
title |
Deep Learning for Iceberg Detection in Satellite Images |
title_short |
Deep Learning for Iceberg Detection in Satellite Images |
title_full |
Deep Learning for Iceberg Detection in Satellite Images |
title_fullStr |
Deep Learning for Iceberg Detection in Satellite Images |
title_full_unstemmed |
Deep Learning for Iceberg Detection in Satellite Images |
title_sort |
deep learning for iceberg detection in satellite images |
publisher |
Uppsala universitet, Institutionen för informationsteknologi |
publishDate |
2021 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-436032 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Iceberg* |
genre_facet |
Arctic Iceberg* |
op_relation |
IT 21010 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-436032 |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1766332299188633600 |