Paper Info
Title
Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network.
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.
Year
DOI
Venue
2018
10.3390/s18020334
SENSORS
Keywords
Field
DocType
ship detection,Gaofen-3,fully convolutional network,truncated statistic,iterative censoring scheme,SAR applications,deep convolutional neural network
Radar imaging,Pattern recognition,Synthetic aperture radar,Clutter,Convolutional neural network,Electronic engineering,Probability distribution,Artificial intelligence,Constant false alarm rate,Gamma distribution,Engineering,Rayleigh distribution
Journal
Volume
Issue
ISSN
18
2.0
1424-8220
Citations 
PageRank 
References 
11
0.71
19
Authors
3
Name
Order
Citations
PageRank
Quanzhi An1141.08
Zongxu Pan2748.13
Hongjian You310317.44