基于小波分解和支持向量机的MSTAR SAR目标分类识别研究  

Research on MSTAR SAR Target Recognition Based on Wavelet Analysis and Support Vector Machine

作　　者：成功[1] 赵巍[1] 潘锦锋[1] 

机构地区：[1]北京航空航天大学电子信息工程学院,北京100083

出　　处：《中国图象图形学报》2009年第2期317-322,共6页Journal of Image and Graphics

基　　金：国家自然科学基金项目(60502019)

摘　　要：对军事目标进行分类是整个SAR ATR过程中最困难的任务。为了进一步提高MSTAR SAR目标的识别效果,在分析了MSTAR SAR图像特点的基础上,提出了一种利用离散小波分解提取目标特征的方法。由于小波分解后的低通近似系数虽然是一种较低分辨率的SAR图像,但是它仍然包含了SAR目标回波的能量,而高通细节系数则包含了目标的细节成份和噪声,因此,可将小波分解后的低通近似系数作为特征,并利用由决策导向循环图扩展的支持向量机来对多类目标进行分类。实验结果表明,即使将3级小波分解后的低通近似系数作为特征,支持向量机的分类精度仍然很高,而且由于特征的数据量较少,因此可使得识别效率得到提高。Military target classification is the most challenging work in SAR ATR. In order to improve the recognition effectand 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.

关 键 词：分类 MSTAR SAR图像 小波分解 支持向量机 

分 类 号：TN391.41[电子电信—物理电子学]