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AIRCRAFT CLASSIFICATION USING IMAGE PROCESSING TECHNIQUES AND ARTIFICIAL NEURAL NETWORKS

机译:基于图像处理技术和人工神经网络的飞机分类

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Identifying the type of an approaching aircraft, should it be a helicopter, a fighter jet or a passenger plane, is an important task in both military and civilian practices. The task in question is normally done by using radar or RF signals. In this study, we suggest an alternative method that introduces the use of a still image instead of RF or radar data. The image was transformed to a binary black and white image, using a Matlab script which utilizes Image Processing Toolbox commands of Matlab, in order to extract the necessary features. The extracted image data of four different types of aircraft was fed into a three-layered feed forward artificial neural network for classification. Satisfactory results were achieved as the rate of successful classification turned out to be 97% on average.
机译:无论是直升机,喷气式战斗机还是客机,识别正在接近的飞机的类型都是军事和民用实践中的重要任务。通常通过使用雷达或RF信号来完成所讨论的任务。在这项研究中,我们建议了一种替代方法,该方法引入了使用静止图像而不是RF或雷达数据的方法。使用Matlab脚本将图像转换为二进制黑白图像,该脚本利用Matlab的“图像处理工具箱”命令来提取必要的特征。将四种不同类型飞机的提取图像数据输入到三层前馈人工神经网络中进行分类。由于成功的分类率平均为97%,因此获得了令人满意的结果。

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