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Very deep learning for ship discrimination in Synthetic Aperture Radar imagery


Schwegmann, CP and Kleynhans, W and Salmon, BP and Mdakane, LW and Meyer, RGV, Very deep learning for ship discrimination in Synthetic Aperture Radar imagery, Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 10-15 July 2016, Beijing, China, pp. 104-107. ISBN 978-1-5090-3333-1 (2016) [Non Refereed Conference Paper]

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DOI: doi:10.1109/IGARSS.2016.7729017


Efficient and effective ship discrimination across multiple Synthetic Aperture Radar sensors is becoming more important as access to SAR data becomes more widespread. A flexible means of separating ships from sea is ideal and can be accomplished using machine learning. Newer, advanced deep learning techniques offer a unique solution but traditionally require a large dataset to train effectively. Highway Networks allow for very deep networks that can be trained using the smaller datasets typical in SAR-based ship detection. A flexible network configuration is possible within Highway Networks due to an adaptive gating mechanism which prevents gradient decay across many layers. This paper presents a very deep High Network configuration as a ship discrimination stage for SAR ship detection. It also presents a three-class SAR dataset that allows for more meaningful analysis of ship discrimination performances. The proposed method was tested on a this SAR dataset and had the highest mean accuracy of all methods tested at 96.67%. The proposed ship discrimination method also provides improved false positive classification compared to the other methods tested.

Item Details

Item Type:Non Refereed Conference Paper
Keywords:synthetic aperture radar, machine learning, marine technology
Research Division:Engineering
Research Group:Communications engineering
Research Field:Signal processing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Salmon, BP (Dr Brian Salmon)
ID Code:117096
Year Published:2016
Web of Science® Times Cited:56
Deposited By:Engineering
Deposited On:2017-05-31
Last Modified:2017-05-31

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