【摘要】 针对不变矩对仿射形变目标描述的不足,为提高舰船型号的识别精度,提出一种基于小波和仿射不变矩特征融合的舰船型号识别方法。首先对二值舰船图像进行归一化处理,并分别提取归一化舰船图像的小波矩特征值和仿射不变矩特征值;然后通过计算样本特征均值与标准差的比值,选择出鲁棒性好、稳定性高的特征,通过归一化方法进行融合;最后构造五类舰船的样本集,采用支持向量机(SVM)作为分类器识别测试样本的型号,分析不同矩特征、样本集大小、SVM参数、本文方法对识别精度、稳定性的影响。实验结果表明,文中给出的算法提高了识别精度,并且在训练样本集较小时仍能获得88%以上的识别率。更多还原

【Abstract】 According to the shortage of invariant moment in deformation object description, in order to improve the recognition accuracy of warship type, this paper proposes a new warship type identification method. First of all, according to the normalized binary images of warship, the features of wavelet moment and affine invariant moment are extracted respectively; Then, the features with good robustness and high stability are selected by calculating the ratio of mean and standard deviation of the sample features, and two different features are fused by normalization method in order to eliminate the differences between two features; Finally, five types of sample set of the warships are constructed through Matlab program, the support vector machine(SVM) is used as classifier to identify the warship type of test sample set which consists of the whole sample set except the training set, and the diffenrences among wavelet moment, affine invariant moment and the proposed method are compared in recognition accuracy and the effect of the training sample set size and the parameters of SVM on the identification accuracy. Experimental results show that the proposed algorithm improves the recognition accuracy, and the recognition accuracy is still greater than 88% when the training sample set is small.更多还原