基于卷积神经网络的SAR图像目标识别研究

摘要

针对合成孔径雷达(Synthetic Aperture Radar, SAR)的图像目标识别应用，该文提出了一种基于卷积神经网络(Convolutional Neural Network, CNN)的SAR图像目标识别方法。首先通过在误差代价函数中引入类别可分性度量，提高了卷积神经网络的类别区分能力；然后利用改进后的卷积神经网络对SAR图像进行特征提取；最后利用支持向量机(Support Vector Machine, SVM)对特征进行分类。使用美国运动和静止目标获取与识别(Moving and Stationary Target Acquisition and Recognition, MSTAR)SAR图像数据进行实验，识别结果证明了所提方法的有效性。%This study presents a new method of Synthetic Aperture Radar (SAR) image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network’s ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.