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Infrared image target recognition of complex background based on curvelet neural network

机译:基于Curvelet神经网络的复杂背景红外图像目标识别

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To improve the recognition rate of the complex background infrared image target, an infrared image target recognition method based on curvelet neural network is proposed. In this method, first of all, all sample images and test images are decomposed by fast discrete curvelet transform. Curvelet coefficients of different scales and various angles are obtained. The low frequency coefficients are applied as characteristic parameter to the SOM neural network for training. Finally, the trained SOM neural network is used for target recognition. The method is not only able to reduce the amount of data to be processed, but also to improve the recognition rate. Simulation results show that the proposed method is superior to the other recognition methods in performance and its recognition rate can reach more than 95 percent.
机译:为了提高复杂背景红外图像目标的识别率,提出了一种基于Curvelet神经网络的红外图像目标识别方法。在这种方法中,首先,所有样本图像和测试图像都通过快速离散curvelet变换进行分解。获得了不同比例和不同角度的曲线波系数。低频系数作为特征参数应用于SOM神经网络进行训练。最后,将训练有素的SOM神经网络用于目标识别。该方法不仅能够减少待处理的数据量,而且能够提高识别率。仿真结果表明,该方法在性能上优于其他识别方法,识别率可达95%以上。

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