Research on MSTAR SAR Target Recognition Based on Wavelet Analysis and Support Vector Machine
基于小波分解和支持向量机的MSTAR SAR目标分类识别研究

Military target classification is the most challenging work in SAR ATR. In order to improve the recognition effect and on the basis of analyzing the characteristic of MSTAR SAR image, a method of discrete wavelet analysis is proposed to extract features. Because wavelet lowpass approximation coefficients contain the energy of SAR target echo and highpass detail coefficients contain the details of target and speckle, the approximation coefficients are obtained as features for classification, although they actually compose a low-resolution SAR image. The decision directed acyclic graph is chosen to improve the classification ability of support vector machine for more than two classes of targets. The experiments results show that high classification probability can be obtained by SVM when the approximation coefficients are used as features by the third level wavelet analysis. Moreover, the size of features is reduced and the recognition method is much more effective.