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An Inshore Ship Detection Method in SAR Images Based on Contextual Fluctuation Information

机译:基于上下文波动信息的SAR图像近海舰船检测方法

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During the inshore ship detection in SAR images, the high similarity between the harbor and the ship body on gray and texture features, resulting in low detection accuracy and high false alarm rate. In this paper, we propose a new approach using contextual fluctuation information to deal with this problem. Firstly, the maximum stability extremal region (MSER) method is used for global pre-screening to quickly obtain candidate targets. Next, the context slice of each candidate target is obtained, then each slice is devided into grids. Distinguish real ship targets from false alarms based on the fluctuations in pixel values within the divided grid. Experimental results based on satellite-borne SAR data illustrate that the proposed method obtains excellent detection performance and low false alarm rate.
机译:在SAR图像中的近海船舶检测过程中,港口与船体之间在灰色和纹理特征上的相似度很高,导致检测精度低和误报率高。在本文中,我们提出了一种使用上下文波动信息来解决此问题的新方法。首先,使用最大稳定极值区域(MSER)方法进行全局预筛选,以快速获得候选目标。接下来,获得每个候选目标的上下文切片,然后将每个切片划分为网格。根据分割后的网格内像素值的波动,将真实的船舶目标与虚假警报区分开。基于卫星SAR数据的实验结果表明,该方法具有良好的检测性能和较低的误报率。

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