基于FABEMD 和Goldstein 滤波器的SAR 舰船尾迹图像增强方法

Q2 Physics and Astronomy 雷达学报 Pub Date : 2013-05-03 DOI:10.3724/SP.J.1300.2012.20059

张问一, 胡东辉, 丁赤飚

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引用次数: 0

摘要

增强SAR 舰船尾迹图像中模糊的开尔文尾迹并保持湍流尾迹特征对舰船及运动参数的反演具有重要作用。该文利用快速自适应2 维经验模式分解方法(Fast and Adaptive Bidimensional Empirical Mode Decomposition,FABEMD)实现图像中开尔文尾迹，湍流尾迹和其他中/大尺度海洋特征的分解，提高开尔文尾迹相对其他特征的图像和频谱对比度。同时引入并改进干涉图Goldstein 滤波器实现对开尔文尾迹的进一步增强，并利用不变矩对增强后的SAR 舰船尾迹图像进行评价。通过原理分析、增强实验和主/客观评价，表明该方法具有显著的开尔文尾迹增强效果，并保持了湍流尾迹特征，实现效率高且适用性较强。

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来源期刊

雷达学报 Physics and Astronomy-Instrumentation

CiteScore

4.10

自引率

0.00%

发文量

882

期刊介绍： Journal of Radars was founded in 2012 by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (formerly the Institute of Electronics) and the China Radar Industry Association (CRIA), which is located in the high-end academic journal and academic exchange platform in the field of radar, and is committed to promoting and leading the scientific and technological development in the field of radar. The journal can publish Chinese papers and English papers, and is now a bimonthly journal. Journal of Radars focuses on theory, originality and foresight, and its scope of coverage mainly includes: radar theory and system, radar signal and data processing technology, radar imaging technology, radar identification and application technology. Journal of Radars has been included in domestic core journals and foreign Scopus, Ei and other databases, and was selected as ‘China's high-quality science and technology journals’, and ranked the first in the category of electronic technology and communication technology in the ‘Chinese Core Journals List (2023 Edition)’.