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Sensors 2018, 18(2), 334; https://doi.org/10.3390/s18020334

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network

1,2,3
,
2,3,* and 1,2,3
1
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China
2
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Received: 11 December 2017 / Revised: 22 January 2018 / Accepted: 22 January 2018 / Published: 24 January 2018
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Abstract

Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach. View Full-Text
Keywords: ship detection; Gaofen-3; fully convolutional network; truncated statistic; iterative censoring scheme; SAR applications; deep convolutional neural network ship detection; Gaofen-3; fully convolutional network; truncated statistic; iterative censoring scheme; SAR applications; deep convolutional neural network
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An, Q.; Pan, Z.; You, H. Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network. Sensors 2018, 18, 334.

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