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Erratum: Raney, R.K. Hybrid Dual-Polarization Synthetic Aperture Radar. Remote Sens. 2019, 11, 1521
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Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images

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Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China
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Center for Satellite Applications on Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China
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School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Department of Geography and Environmental Management and Department of Systems Design Engineering University of Waterloo, Waterloo, ON N2L 3G1, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(18), 2171; https://doi.org/10.3390/rs11182171
Received: 30 May 2019 / Revised: 12 August 2019 / Accepted: 11 September 2019 / Published: 18 September 2019
(This article belongs to the Special Issue Compact Polarimetric SAR)
Compact polarimetric synthetic aperture radar (CP SAR), as a new technique or observation system, has attracted much attention in recent years. Compared with quad-polarization SAR (QP SAR), CP SAR provides an observation with a wider swath, while, compared with linear dual-polarization SAR, retains more polarization information in observations. These characteristics make CP SAR a useful tool in marine environmental applications. Previous studies showed the potential of CP SAR images for ship detection. However, false alarms, caused by ocean clutter and the lack of detailed information about ships, largely hinder traditional methods from feature selection for ship discrimination. In this paper, a segmentation method designed specifically for ship detection from CP SAR images is proposed. The pixel-wise detection is based on a fully convolutional network (i.e., U-Net). In particular, three classes (ship, land, and sea) were considered in the classification scheme. To extract features, a series of down-samplings with several convolutions were employed. Then, to generate classifications, deep semantic and shallow high-resolution features were used in up-sampling. Experiments on several CP SAR images simulated from Gaofen-3 QP SAR images demonstrate the effectiveness of the proposed method. Compared with Faster RCNN (region-based convolutional neural network), which is considered a popular and effective deep learning network for object detection, the newly proposed method, with precision and recall greater than 90% and a F1 score of 0.912, performs better at ship detection. Additionally, findings verify the advantages of the CP configuration compared with single polarization and linear dual-polarization. View Full-Text
Keywords: compact polarimetric SAR; ship detection; fully convolutional network; semantic segmentation; Gaofen-3 compact polarimetric SAR; ship detection; fully convolutional network; semantic segmentation; Gaofen-3
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Fan, Q.; Chen, F.; Cheng, M.; Lou, S.; Xiao, R.; Zhang, B.; Wang, C.; Li, J. Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images. Remote Sens. 2019, 11, 2171.

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