AIS Data Aided Rayleigh CFAR Ship Detection Algorithm of Multiple-Target Environment in SAR Images

被引:26
作者
Ai, Jiaqiu [1 ,2 ]
Pei, Zhilin [1 ,2 ]
Yao, Baidong [3 ]
Wang, Zhaocheng [4 ,5 ]
Xing, Mengdao [6 ]
机构
[1] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China
[2] Hefei Univ Technol, Intelligent Interconnected Syst, Hefei 230009, Peoples R China
[3] China Elect Technol Corp, Inst 38, Beijing 100846, Peoples R China
[4] Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin 300130, Peoples R China
[5] Tianjin Inst Adv Technol, Tianjin 300308, Peoples R China
[6] Xidian Univ, Acad Adv Interdisciplinary Res, Xian 710071, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Clutter; Artificial intelligence; Detectors; Marine vehicles; Synthetic aperture radar; Parameter estimation; Radar polarimetry; SAR ship detection; Constant false alarm rate (CFAR); Automatic Identification System (AIS) information; Adaptive-depth based clutter trimming; Closed-form solution; TERRASAR-X IMAGES; RESOLUTION; STATISTICS; MODEL; SURVEILLANCE; PROCESSORS;
D O I
10.1109/TAES.2021.3111849
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
This article proposes an automatic identification system (AIS) data aided Rayleigh constant false alarm rate (AIS-RCFAR) ship detection algorithm of multiple-target environment in synthetic aperture radar (SAR) images. This method aims to improve the detection performance in complex environment with the aid of AIS data. Traditional CFAR detectors generally use all the samples in the local background window for parameter estimation. However, in multiple-target environment, clutter edges and transition areas, due to the interference of the high-intensity outliers, such as target pixels, ghosts, and other interfering pixels, the parameters are often overestimated, causing degradation of the detection performance. Aiming at solving this problem, AIS-RCFAR designs an adaptive-threshold based clutter trimming method with an adaptive-trimming-depth aided by AIS data to effectively eliminate the high-intensity outliers in the local background window while greatly sustaining the real sea clutter samples. Maximum-likelihood-estimator with a closed-form solution is proposed to precisely estimate the parameters using the adaptively-trimmed clutter samples, the probability density function of the sea clutter following Rayleigh distribution can be accurately modeled. AIS-RCFAR greatly enhances the detection rate in both homogeneous and nonhomogeneous multiple-target environment, it also achieves a very low false alarm rate. In addition, the whole procedure of AIS-RCFAR is simple and efficient. Simulated data and real SAR images with corresponding matched AIS data are used for experiments to validate the superiority and feasibility of AIS-RCFAR.
引用
收藏
页码:1266 / 1282
页数:17
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