基于免疫克隆高斯过程隐变量模型的SAR目标特征提取与识别

作为一种非线性维数约减算法,高斯过程隐变量模型(Gaussian process latent variable model,GPLVM)由于其适合处理小样本、高维数据,因而在模式识别、计算机视觉等领域得到了广泛应用.基于此,提出一种基于改进GPLVM的SAR图像目标特征提取及自动识别方法,其中利用改进的GPLVM进行特征提取,高斯过程分类进行目标识别.传统GPLVM使用共轭梯度法对似然函数进行优化,为避免梯度估值易受噪声干扰、步长对算法影响严重等缺点,提出基于免疫克隆选择算法的GPLVM,利用其具有快速收敛到全局最优的特性提高算法性能.实验结果表明,该算法不仅降低了特征维数,且提高了识别精度,从而验证了算法用于SAR图像目标识别的有效性.

Gaussian process latent variable model based on immune clonal selection for SAR target feature extraction and recognition

As an unsupervised dimension reduction algorithm, Gaussian process latent variable model (GPLVM) has been widely applied in pattern recognition, and computer vision for its capability in dealing with small samples and high dimensional data samples. As GPLVM can discover low dimensional manifolds in high dimensional data given only a small number of examples, a new SAR target recognition method is proposed which combines Gaussian process latent variable model as the feature extraction technology and employs Gaussian process classification as the classifier. In GPLVM, the likelihood is optimized by using the scaled conjugate gradient. In order to avoid the noise effect to gradient estimate and overcome the disadvantage that the performance is severely affected by the step length, the immune clone algorithm based GPLVM is proposed for feature extraction where the immune clonal selection algorithm characterized by rapid convergence to global optimum is utilized to improve the performance. The experimental results show that the method not only reduces the problem dimension but also gets better recognition accuracy.