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Detection of sea surface small targets in infrared images based on multilevel filler and minimum risk Bayes test

机译:基于多级填料和最小风险贝叶斯检测的红外图像海面小目标检测

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This paper discusses the research in small target detection in infrared images with heavy clutter background. For most infrared images, ship objects are rather dim in the relative dark sea surface background. The existence of scan line disturbance and noise also increases the difficulty in proper detection. Dim objects must be distinguished from a dark background. On the other hand, the small targets must also be distinguished from clutters. Through analysis of the targets and background, we build characteristic models of small ship objects, noise and sea backgrounds respectively, and indicate their differences in spatial and frequency domains among them. Based on the principles of signal processing, pattern recognition and artificial intelligence, we propose a combined algorithm for detecting sea surface small targets. In this algorithm, components of background and noise are first suppressed by a multilevel filter designed accordingly, meanwhile enhancing the target ones of interest. The pixels of the candidate targets are then discriminated by minimum risk Bayes test. Finally, according to a priori knowledge about the targets such as the ranges of their sizes, the targets of interest can be detected. In particular, the related probability distributions used by statistic decision are obtained by offline learning of typical training samples. Experiments show that the algorithm is excellent for such kinds of target detection and is robust to noise.
机译:本文讨论了沉重杂物背景红外图像小目标检测研究。对于大多数红外图像,船舶对象在相对暗海表面背景中相当暗淡。扫描线干扰和噪声的存在也增加了正确检测的难度。暗淡物体必须与深色背景区分开。另一方面,少量目标也必须与丛纹有区别。通过分析目标和背景,我们分别构建小型船舶物体,噪声和海背景的特征模型,并表明它们在其中的空间和频率域中的差异。基于信号处理,模式识别和人工智能的原理,我们提出了一种检测海面小目标的组合算法。在该算法中,首先通过相应地设计的多级滤波器来抑制背景和噪声的组件,同时增强感兴趣的目标。然后通过最小的风险贝叶斯测试区分候选目标的像素。最后,根据关于其尺寸范围的目标的先验知识,可以检测感兴趣的目标。特别地,通过典型训练样本的离线学习获得统计决定使用的相关概率分布。实验表明,该算法对于这种类型的目标检测非常优异,并且对噪声具有鲁棒性。

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