Distinction between ships and icebergs in SAR images using ensemble loss trained convolutional neural networks

Zhang, X; Liu, Y; Zheng, Y; Zhao, Z; Li, J

Distinction between ships and icebergs in SAR images using ensemble loss trained convolutional neural networks

Zhang, X

Liu, Y

Zheng, Y Zhao, Z

Li, J

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11320 LNAI pp. 216 - 223
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Zhang, X https://orcid.org/0000-0002-3783-6560	en_US
dc.contributor.author	Liu, Y https://orcid.org/0000-0002-7680-3155	en_US
dc.contributor.author	Zheng, Y	en_US
dc.contributor.author	Zhao, Z https://orcid.org/0000-0001-5544-4504	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11320 LNAI pp. 216 - 223	en_US
dc.identifier.isbn	9783030039905	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129149
dc.description.abstract	© Springer Nature Switzerland AG 2018. With the phenomenon of global warming, more new shipping routes will be open and utilized by more and more ships in the polar regions, particularly in the Arctic. Synthetic aperture radar (SAR) has been widely used in ship and iceberg monitoring for maritime surveillance and safety in the Arctic waters. At present, compared with the object detection of ship or iceberg, the task of ship and iceberg distinction in SAR images is still in challenge. In this work, we propose a novel loss function called ensemble loss to train convolutional neural networks (CNNs), which is a convex function and incorporates the traits of cross entropy and hinge loss. The ensemble loss trained CNNs model for the distinction between ship and iceberg is evaluated on a real-world SAR data set, which can get a higher classification accuracy to 90.15%. Experiment on another real image data set also confirm the effectiveness of the proposed ensemble loss.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-03991-2_22	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Distinction between ships and icebergs in SAR images using ensemble loss trained convolutional neural networks	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11320 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.declined	2018-12-20T10:45:12.431+1100
pubs.publication-status	Published	en_US
pubs.volume	11320 LNAI	en_US

Abstract:

© Springer Nature Switzerland AG 2018. With the phenomenon of global warming, more new shipping routes will be open and utilized by more and more ships in the polar regions, particularly in the Arctic. Synthetic aperture radar (SAR) has been widely used in ship and iceberg monitoring for maritime surveillance and safety in the Arctic waters. At present, compared with the object detection of ship or iceberg, the task of ship and iceberg distinction in SAR images is still in challenge. In this work, we propose a novel loss function called ensemble loss to train convolutional neural networks (CNNs), which is a convex function and incorporates the traits of cross entropy and hinge loss. The ensemble loss trained CNNs model for the distinction between ship and iceberg is evaluated on a real-world SAR data set, which can get a higher classification accuracy to 90.15%. Experiment on another real image data set also confirm the effectiveness of the proposed ensemble loss.

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http://hdl.handle.net/10453/129149