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Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images
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Validating GEV Model for Reflection Symmetry-Based Ocean Ship Detection with Gaofen-3 Dual-Polarimetric Data

1
School of Automation, Northwestern Polytechnical Universtiy, Xi’an 710129, China
2
Huawei Technologies Co. Ltd., Shenzhen 518129, China
3
School of Electronics and Information, Northwestern Polytechnical Universtiy, Xi’an 710129, China
4
School of Electronic Engineering, Xidian Universtiy, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1148; https://doi.org/10.3390/rs12071148
Received: 3 March 2020 / Revised: 31 March 2020 / Accepted: 1 April 2020 / Published: 3 April 2020
The spaceborne synthetic aperture radar (SAR) is quite powerful in worldwide ocean observation, especially for ship monitoring, as a hot topic in ocean surveillance. The launched Gaofen-3 (GF3) satellite of China can provide C-band and multi-polarization SAR data, and one of its scientific applications is ocean ship detection. Compared with the single polarization system, polarimetric systems can be used for more effective ship detection. In this paper, a generalized extreme value (GEV)-based constant false alarm rate (CFAR) detector is proposed for ship detection in the ocean by using the reflection symmetry metric of dual-polarization. The reflection symmetry property shows big differences between the metallic targets at sea and the sea surface. In addition, the GEV statistical model is employed for reflection symmetry statistical distribution, which fits the reflection symmetry probability density function (pdf) well. Five dual-polarimetric GF3 stripmap ocean data sets are introduced in the paper, to show the contrast in enhancement by using reflection symmetry and to investigate the GEV model fit to the reflection symmetry metric. Additionally, with the detection experiments on the real GF3 datasets, the effectiveness and efficiency of the GEV model for reflection symmetry and the model-based ocean ship detector are verified. View Full-Text
Keywords: dual-polarimetric; ship detection; reflection symmetry; generalized extreme value (GEV) distribution; Gaofen-3 (GF3) dual-polarimetric; ship detection; reflection symmetry; generalized extreme value (GEV) distribution; Gaofen-3 (GF3)
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Guo, R.; Cui, J.; Jing, G.; Zhang, S.; Xing, M. Validating GEV Model for Reflection Symmetry-Based Ocean Ship Detection with Gaofen-3 Dual-Polarimetric Data. Remote Sens. 2020, 12, 1148.

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