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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Holism-based features for target classification in focused and complex-valued synthetic aperture radar imagery
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Holism-based features for target classification in focused and complex-valued synthetic aperture radar imagery

机译：聚焦和复数值合成孔径雷达图像中基于整体的目标分类功能

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摘要

Reductionism and holism are two worldviews underlying the fields of linear and nonlinear signal processing, respectively. Conventional radar resolution theory is motivated by the former view, and it is violated by nonlinear phase modulation induced by dispersive scattering typically associated with extended targets. Motivated by the latter view, this paper offers a new insight into the process of feature extraction for target-recognition applications in single-channel imagery output from synthetic aperture radar processors. Two novel frameworks for holism-based feature extraction are presented. The first framework is based solely on the often-ignored phase chip. The second framework uses the complex-valued 2-D synthetic aperture radar chip after it is transformed into a 1-D vector. Representative features are introduced under each framework. Further, for comparison purposes, baseline features from the power-detected chip are also considered. Three feature sets are extracted from the real-world MSTAR data set and used separately and combinatorially to design multiple instances of an eight-class support vector machine classifier. A classification accuracy of 93.42% is achieved for the holism-based features. This is in comparison to 73.63% for the baseline features. Using Fisher scoring to measure the information contained in each feature, top-ranked features from the first and second holism-based frameworks, respectively, are found to be 7 and 160 times those of the baseline features. Because the nonlinear phenomenon is resolution dependent, our proposed approach is expected to achieve even greater accuracy for synthetic aperture radar sensors with higher resolution.
机译：还原论和整体论分别是线性和非线性信号处理领域的两个世界观。常规的雷达分辨率理论是由前一种观点激发的，但通常被与扩展目标相关的色散散射所引起的非线性相位调制所破坏。基于后一种观点，本文为合成孔径雷达处理器输出的单通道图像中目标识别应用的特征提取过程提供了新的见解。提出了两种基于整体的特征提取的新颖框架。第一个框架仅基于经常被忽略的相位芯片。第二种框架在将复值二维合成孔径雷达芯片转换为一维矢量后使用它。每个框架下都介绍了代表性的功能。此外，出于比较目的，还考虑了功率检测芯片的基线特征。从现实世界的MSTAR数据集中提取了三个特征集，并分别和组合使用它们来设计八类支持向量机分类器的多个实例。基于整体特征的分类精度达到93.42％。相比之下，基线特征为73.63％。使用费舍尔评分来衡量每个要素中包含的信息，发现第一和第二基于整体论的框架中排名最高的要素分别是基线要素的7倍和160倍。由于非线性现象与分辨率有关，因此对于具有更高分辨率的合成孔径雷达传感器，我们提出的方法有望获得更高的精度。

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