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Content Provider | IET Digital Library |
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Author | Ao, Wei Xu, Feng Qian, Yutong Guo, Qian |
Abstract | Synthetic aperture radar (SAR) is one of the most important and widely-used tools of large scale marine surveillance. Constant false alarm rate is tailored for maritime metallic target detection in SAR images. However, it is very difficult to distinguish ship targets from other metallic targets. Some manual features have been designed to discriminate ship and non-ship target. These manual features often overfit some scenarios but fail to other situations. Recently, deep convolutional networks have achieved impressive success in optical image classification, which encourages scholars of remote sensing society to leverage this powerful tool to extract robust features of SAR ship targets. This study adopts VGG-16 to extract features of ship and non-ship targets. Then, unsupervised clustering is conducted to group ship and non-ship targets, respectively, using the extracted features, which can effectively tackle intraspecies variants of ship and non-ship targets. Finally, these representative feature vectors are utilised to differentiate ship and non-ship targets of other scenarios. The proposed algorithms are extensively tested undertaken on 11 ALOS-2 SAR images, which demonstrate good performance of the methods. |
Starting Page | 6920 |
Ending Page | 6922 |
Page Count | 3 |
Volume Number | 2019 |
e-ISSN | 20513305 |
Issue Number | Issue 20, Oct (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/20 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0564 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2019-07-11 |
Access Restriction | Open |
Rights License | Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) |
Subject Keyword | 11 ALOS-2 SAR Images Computer Vision And Image Processing Technique Differentiate Ship Feature Clustering Based Discrimination Feature Extraction Geophysical Image Processing Geophysical Technique And Equipment Image Classification Image Recognition Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Manual Feature Maritime Metallic Target Detection Metallic Targets Nonship Targets Object Detection Optical, Image And Video Signal Processing Pattern Clustering Radar Equipment Radar Imaging Remote Sensing by Radar SAR Ship Targets Ships Synthetic Aperture Radar System And Application |
Content Type | Text |
Resource Type | Article |
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