Ship Detection in PolSAR Images Based on a Modified Polarimetric Notch Filter

被引:4
作者
Zhou, Xiangyu [1 ]
Li, Tao [1 ,2 ]
机构
[1] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Peoples R China
[2] Nanjing Res Inst Elect Technol, Nanjing 210039, Peoples R China
基金
中国国家自然科学基金;
关键词
polarimetric synthetic aperture radar (PolSAR); ship detection; polarimetric notch filter (PNF); azimuth ambiguity removal; AZIMUTH AMBIGUITIES; CFAR DETECTOR; SPECKLE REDUCTION; SCATTERING MODEL; SAR DATA; REMOVAL; DECOMPOSITION; SCHEME; SEA;
D O I
10.3390/electronics12122683
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Ship detection based on synthetic aperture radar (SAR) imagery is one of the key applications for maritime security. Compared with single-channel SAR images, polarimetric SAR (PolSAR) data contains the fully-polarized information, which better facilitates better discriminating between targets, sea clutter, and interference. Therefore, many ship detection methods based on the polarimetric scattering mechanism have been studied. To deal with the false alarms caused by the existence of ghost targets, resulting from azimuth ambiguities and interference from side lobes, a modified polarimetric notch filter (PNF) is proposed for PolSAR ship detection. In the proposed method, the third eigenvalue obtained by the eigenvalue-eigenvector decomposition of the polarimetric covariance matrix is utilized to construct a new feature vector. Then, the target power can be computed to construct the modified PNF detector. On the one hand, the detection rate of ship targets can be enhanced by target-to-clutter contrast. On the other hand, false alarms resulting from azimuth ambiguities and side lobes can be reduced to an extent. Experimental results based on three C-band AIRSAR PolSAR datasets demonstrated the capability of the proposed PNF detector to improve detection performance while reducing false alarms. To be specific, the figure of merit (FoM) of the proposed method is the highest among comparative approaches with results of 80%, 100%, and 100% for the tested datasets, respectively.
引用
收藏
页数:18
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