TY - GEN
T1 - Multi-view SAS image classification using deep learning
AU - Williams, David P.
AU - Dugelay, Samantha
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - A new approach is proposed for multi-view classification when sonar data is in the form of imagery and each object has been viewed an arbitrary number of times. An image-fusion technique is employed in conjunction with a deep learning algorithm (based on Boltzmann machines) so that the sonar data from multiple views can be combined and exploited at the (earliest) image level. The method utilizes single-view imagery and, whenever available, multi-view fused imagery, in the same unified classification framework. The promise of the proposed approach is demonstrated in the context of an object classification task with real synthetic aperture sonar (SAS) imagery collected at sea.
AB - A new approach is proposed for multi-view classification when sonar data is in the form of imagery and each object has been viewed an arbitrary number of times. An image-fusion technique is employed in conjunction with a deep learning algorithm (based on Boltzmann machines) so that the sonar data from multiple views can be combined and exploited at the (earliest) image level. The method utilizes single-view imagery and, whenever available, multi-view fused imagery, in the same unified classification framework. The promise of the proposed approach is demonstrated in the context of an object classification task with real synthetic aperture sonar (SAS) imagery collected at sea.
UR - http://www.scopus.com/inward/record.url?scp=85006868123&partnerID=8YFLogxK
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U2 - 10.1109/OCEANS.2016.7761334
DO - 10.1109/OCEANS.2016.7761334
M3 - Conference contribution
AN - SCOPUS:85006868123
T3 - OCEANS 2016 MTS/IEEE Monterey, OCE 2016
BT - OCEANS 2016 MTS/IEEE Monterey, OCE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 OCEANS MTS/IEEE Monterey, OCE 2016
Y2 - 19 September 2016 through 23 September 2016
ER -