摘要
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.
As a nonlinear 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 size and high-dimension- al samples. As GPLVM can discover low-dimensional manifolds in high-dimensional data given only a small number of samples, a new SAR target recognition method was proposed, in which a modified GPLVM was used for feature extrac- tion and Gaussian process classification was employed as the classifier. In GPLVM, the likelihood was optimized by u- sing 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 selection algorithm based GPLVM was de- veloped for target feature extraction where the immune clonal selection algorithm characterized by rapid convergence to global optimum was utilized to improve the performance. The experimental results show that the method not only reduces the dimension but also gets higher accuracy.
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2013年第3期231-236,共6页
Journal of Infrared and Millimeter Waves
基金
国家自然科学基金(61272282
61072106
61203303
61272279)
陕西省自然科学基金(2011JQ8020)
中央高校基本科研业务费专项资金资助(JY10000902001
JY10000902045
K50511020011)~~
关键词
高斯过程隐变量模型
免疫克隆选择算法
特征提取
SAR图像目标识别
Gaussian process latent variable model
immune clonal selection algorithm
featrue extraction
SAR target recognition
