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首页> 外文期刊>Radar, Sonar & Navigation, IET >Analysis of polarimetric techniques using high-resolution polarimetry data in an automatic target recognition context
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Analysis of polarimetric techniques using high-resolution polarimetry data in an automatic target recognition context

机译：在自动目标识别上下文中使用高分辨率偏振仪数据分析偏振仪技术

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摘要

In recent years, large numbers of radar images are collected but there is neither time nor enough manpower to go through each collected image. Researchers in the automatic target recognition (ATR) field have developed automated algorithms and tools to analyse each image and obtain higher recognition rate and fewer false alarms but there is still a need for improvement in these aspects. In this study, we have investigated various polarimetric and non-polarimetric techniques and recommended the best ATR approach among those analysed for higher recognition rate and least false alarm rate. The experimental results show that self-organising map (SOM) feature extraction technique with a two-dimensional Fourier transform (2DFFT) algorithm has a better classification rate and a lower false alarm rate. The classifier used here was AND Corporation's holographic neural technology (HNeT) classifier. The SOM technique using |HH|, |HV| and |VV| achieved 98.9% correct classification over the detected targets and reduced the false alarm rate to 8.2%. An ATR system trained with both target and not-a-target class data produced a lower false alarm rate compared with ATR systems trained with target samples alone. This study will help in selection of appropriate methods for future ATR system implementations. In addition, it will assist image analysts (IAs) in choosing appropriate techniques and training datasets to perform their operational tasks.
机译：近年来，收集了大量雷达图像，但是没有时间也没有足够的人力来遍历每个收集的图像。自动目标识别（ATR）领域的研究人员已经开发了自动算法和工具来分析每个图像并获得更高的识别率和更少的错误警报，但是仍然需要在这些方面进行改进。在这项研究中，我们研究了各种极化和非极化技术，并推荐了在分析中具有较高识别率和最低误报率的最佳ATR方法。实验结果表明，采用二维傅里叶变换（2DFFT）算法的自组织图特征提取技术具有较好的分类率和较低的误报率。此处使用的分类器是AND Corporation的全息神经技术（HNeT）分类器。使用| HH |，| HV |的SOM技术和| VV |在检测到的目标上实现了98.9％的正确分类，并将误报率降低到8.2％。与仅使用目标样本进行培训的ATR系统相比，使用目标和非目标类别数据进行培训的ATR系统产生的误报率较低。这项研究将有助于为将来的ATR系统实施选择合适的方法。此外，它将协助图像分析人员（IAs）选择适当的技术并训练数据集以执行其操作任务。

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