基于稀疏求解的改进PCA方法在SAR目标识别中的应用

Improved PCA method for SAR target recognition based on sparse solution

针对实际军事情况下车辆目标为非合作目标,提出改进的主成分分析方法(IPCA)。它首先利用稀疏求解方法得到与测试样本最相关的部分训练样本以及它们对测试样本的表示系数。然后结合主成分分析(PCA)得到最优投影矩阵,使投影后不同测试样本能更好地利用训练样本信息进行分类。利用美国运动和静止目标获取与识别数据库中3类目标进行识别实验,结果表明基于改进的PCA方法比传统的PCA方法能够得到更高的识别率,并对稀疏方位角训练样本有更好的鲁棒性。

In this work,an improved principal component analysis method（IPCA） is proposed for SAR target recognition.Firstly,we use the sparse method to obtain the training samples which are most relevant to the test samples and their representation coefficients for the test samples.Then,using the principal component analysis（PCA） we obtain the optimal projection matrix so that different test samples after projection can be better classified by using the training sample information.The results of experiments,performed on SAR ground stationary targets based on the moving and stationary target acquisition and recognition（MSTAR） database,show that IPCA reaches higher recognition rate and better robustness to sparse aspect training samples of three true objects than PCA.