摘要
基于支撑矢量机对二值遥感图像飞机目标进行了识别 ,与欧氏距离判别法和神经网络方法的识别结果比较 ,表明对以矢量表示的高维二值为特征的图像识别问题 ,支撑矢量机方法具有良好的推广能力。而且 ,不同图像的二值化取值范围对识别结果有着直接的影响。
The recognition results of image targets by existing methods (such as neural network and Euclid distance methods) are not satisfactory for shaded image or 3-D rotation image. We present an improved SVM (support vector machine) method for recognition of such images. First, remote sensing images are processed into binary images. Then, the binary images are nomalized according to the kernel function used in SVM (where the kernel function is Gaussian, the range of normalization is 0 to 0.02), and the normalized image targets are divided into two sets, one is for training, another is for testing. After that, SVM is trained by the training set. Finally, the trained SVM is used to test the testing set. We find that the normalization process is crucial for the application of SVM. Our main contribution is in the formula for normalization of targets as given in Eq. (7), where C is a constant which is determined by the kernel function and experiments. Table 1 shows the effect of constant C on the correct recognition rate. Comparisons as given in Table 2 show that the correct recognition rate of the improved SVM method is 96%, while that of neural network method and Euclid distance method are 90% and 84% respectively.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2002年第4期536-539,共4页
Journal of Northwestern Polytechnical University
基金
航空基础研究基金 (0 0 F5 30 5 0 )
雷达信号处理重点实验室基金 (2 0 0 0 JS0 1.4.1.HK0 311)
西北工业大学高层次人才基金
