Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination
Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established...
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ftmdpi:oai:mdpi.com:/2220-9964/9/12/758/ 2023-08-20T04:04:12+02:00 Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination Frederik Seerup Hass Jamal Jokar Arsanjani agris 2020-12-19 application/pdf https://doi.org/10.3390/ijgi9120758 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/ijgi9120758 https://creativecommons.org/licenses/by/4.0/ ISPRS International Journal of Geo-Information; Volume 9; Issue 12; Pages: 758 deep learning object detection ocean objects synthetic aperture radar classification YoloV3 Text 2020 ftmdpi https://doi.org/10.3390/ijgi9120758 2023-08-01T00:41:55Z Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data. Text Arctic Climate change Greenland Iceberg* MDPI Open Access Publishing Arctic Greenland ISPRS International Journal of Geo-Information 9 12 758 |
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Open Polar |
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MDPI Open Access Publishing |
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language |
English |
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deep learning object detection ocean objects synthetic aperture radar classification YoloV3 |
spellingShingle |
deep learning object detection ocean objects synthetic aperture radar classification YoloV3 Frederik Seerup Hass Jamal Jokar Arsanjani Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination |
topic_facet |
deep learning object detection ocean objects synthetic aperture radar classification YoloV3 |
description |
Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data. |
format |
Text |
author |
Frederik Seerup Hass Jamal Jokar Arsanjani |
author_facet |
Frederik Seerup Hass Jamal Jokar Arsanjani |
author_sort |
Frederik Seerup Hass |
title |
Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination |
title_short |
Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination |
title_full |
Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination |
title_fullStr |
Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination |
title_full_unstemmed |
Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination |
title_sort |
deep learning for detecting and classifying ocean objects: application of yolov3 for iceberg–ship discrimination |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/ijgi9120758 |
op_coverage |
agris |
geographic |
Arctic Greenland |
geographic_facet |
Arctic Greenland |
genre |
Arctic Climate change Greenland Iceberg* |
genre_facet |
Arctic Climate change Greenland Iceberg* |
op_source |
ISPRS International Journal of Geo-Information; Volume 9; Issue 12; Pages: 758 |
op_relation |
https://dx.doi.org/10.3390/ijgi9120758 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/ijgi9120758 |
container_title |
ISPRS International Journal of Geo-Information |
container_volume |
9 |
container_issue |
12 |
container_start_page |
758 |
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