Ship Detection Based on Complex Signal Kurtosis in Single-Channel SAR Imagery

被引:89
作者
Leng, Xiangguang [1 ]
Ji, Kefeng [1 ]
Zhou, Shilin [1 ]
Xing, Xiangwei [2 ]
机构
[1] Natl Univ Def Technol, Coll Elect Sci & Technol, Changsha 410073, Hunan, Peoples R China
[2] Beijing Inst Remote Sensing Informat, Beijing 100192, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2019年 / 57卷 / 09期
基金
中国国家自然科学基金;
关键词
Complex signal kurtosis (CSK); complex-valued; non-Gaussianity; noncircularity; ship detection; synthetic aperture radar (SAR); SYNTHETIC-APERTURE RADAR; CIRCULARITY; TARGETS; PHASE;
D O I
10.1109/TGRS.2019.2906054
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Recent studies have shown that complex information in single-channel synthetic aperture radar (SAR) imagery has practically always been underrated. This improves the perception of their potential for ocean monitoring. Based on the in-depth interpretation of complex signal kurtosis (CSK), this paper proposes a new ship detection method based on CSK in single-channel SAR imagery. The proposed method consists of two main parts, i.e., region proposal and target identification. The basic idea is to first detect potential ship locations based on the region proposal. Then, the final ship target is acquired based on the target identification. Compared to conventional methods based on detected products, e.g., the constant false alarm rate (CFAR), the proposed method has three advantages. First, CSK can take advantage of both non-Gaussianity and noncircularity, which is the fundamental concept distinguishing complex signal analysis from the real case. Second, the proposed method can be intrinsically free of false alarms caused by radio frequency interference (RFI). Finally, the proposed method can avoid missing detection in dense target situations. This methodology has been demonstrated over significant data sets acquired from Sentinel-1, TerraSAR-X, and Gaofen-3. These results validate that CSK is a vital indicator of ship detection. Complex information is expected to play a more important role in single-channel SAR imagery.
引用
收藏
页码:6447 / 6461
页数:15
相关论文
共 51 条
[1]   Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network [J].
An, Quanzhi ;
Pan, Zongxu ;
You, Hongjian .
SENSORS, 2018, 18 (02)
[2]  
Anfinsen S. N., 2012, P IEEE INT GEOSC REM, P1
[3]  
[Anonymous], 2017, SENTINELS SCI DATA H
[4]  
[Anonymous], 2017, SENTINEL TOOLBOX EXP
[5]  
[Anonymous], 2018, OPENSARSHIPFILTER PA
[6]   Subband Extraction Strategies in Ship Detection With the Subaperture Cross-Correlation Magnitude [J].
Brekke, Camilla ;
Anfinsen, Stian Normann ;
Larsen, Yngvar .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2013, 10 (04) :786-790
[7]   Ship Surveillance With TerraSAR-X [J].
Brusch, Stephan ;
Lehner, Susanne ;
Fritz, Thomas ;
Soccorsi, Matteo ;
Soloviev, Alexander ;
van Schie, Bart .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 49 (03) :1092-1103
[8]  
Crisp David James, 2013, 2013 International Conference on Radar, P318, DOI 10.1109/RADAR.2013.6652006
[9]  
Crisp D. J., 2004, RR0272 DSTO INF SCI
[10]  
Cumming I.G., 2004, DIGIT SIGNAL PROCESS