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首页> 外文期刊>International journal of computer science and network security >Automatic Target Recognition of SAR Images Using Radial Features and SVM
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Automatic Target Recognition of SAR Images Using Radial Features and SVM

机译：使用径向特征和SVM的SAR图像自动目标识别

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The armed forces use a variety of sensor information to locate and target enemy forces. Because of the large area and sparse population, the surveillance becomes a difficult problem. With technological advances, the armed forces can rely upon different types of image data like, infrared data, and radar data. Due to the enormous amount of data, it becomes very difficult to analyse the data without pre-processing and later to detect or classify target data. To full fill this gap this paper reports an algorithm for automatic target recognition in the battle field. This work focuses on synthetic aperture radar (SAR) images for recognizing enemy targets with more accuracy. The available images (MSTAR public open database which is freely available in open literature) is used for experimentation and training the SVM (Support vector Machine). The data will be pre-process first. The pre-processing is required to distinguish the target from clutters like building, trees etc., and non-target objects such as confuse vehicles etc., which is very much required for identifying the targets like Battle tank or armoured personnel carrier effectively. The clutters create noise which is to be removed in pre-processing. The algorithm will help to recognize specific class of the targets e.g., T-72 tank on the basis of target signature. The automatic target recognition is based on the location and orientation. The SVM is used for the classification. SVM are trained and validated with a test set to determine the best performance. The resulting SVM has a recognition rate of 98%.
机译：武装部队使用各种传感器信息来定位和瞄准敌军。由于面积大，人口稀少，监视成为一个难题。随着技术的进步，武装部队可以依靠不同类型的图像数据，例如红外数据和雷达数据。由于数据量巨大，如果不进行预处理就很难分析数据，而后又无法检测或分类目标数据。为了填补这一空白，本文报告了一种用于战场自动目标识别的算法。这项工作专注于合成孔径雷达（SAR）图像，以更准确地识别敌人目标。可用的图像（MSTAR公共开放数据库，可以在开放文献中免费获得）用于实验和训练SVM（支持向量机）。数据将首先进行预处理。需要进行预处理以将目标与杂物（例如建筑物，树木等）和非目标对象（例如混乱的车辆等）区分开，这对于有效识别战车或装甲运兵车等目标是非常必要的。杂物会产生噪音，需要在预处理时将其消除。该算法将有助于根据目标特征识别目标的特定类别，例如T-72坦克。自动目标识别基于位置和方向。 SVM用于分类。通过测试集对SVM进行培训和验证，以确定最佳性能。生成的SVM的识别率为98％。

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