摘要
提出了一种基于非线性核空间映射人工免疫网络的高光谱遥感图像分类算法.根据生物免疫网络基本原理构建了人工免疫网络模型,利用非线性核函数将高光谱训练样本映射到高维空间,完善了人工免疫网络中目标样本核空间相似性分选方法,降低了人工免疫网络识别样本所需的抗体数量,提升了算法的分类精度和运算效率.为了验证算法的有效性,利用两组高光谱遥感数据将多种高光谱分类方法进行了对比实验.实验表明该算法分类精度和算法运算时间上都有较大改善,是一种分类精度更高、运算速度更快的改进型基于人工免疫网络的高光谱遥感图像分类新方法.
A novel classification algorithm of hyperspectral remote sensing image based on nonlinear kernel mapping artificial immune network was proposed. An artificial immune network model was constructed according to natural immune network theory. The training samples of hyperspectral imagery are projected to high feature space with nonlinear kernel function, which improved the sorting method based on similarity in kernel space in artificial immune network. The number of antibodies which are used to recognize training samples is reduced, and the accuracy and efficiency of the algorithm are enhanced. To evaluate the advantage of the proposed algorithm, some other kinds of hyperspectral image classification algorithms were compared with it in the experiment using two hyperspectral image data. Experimental results demonstrated that the proposed algorithm, which acquires higher accuracy and computing speed than traditional hyperspectral image classification algorithms, is a new improved classification algorithm of hyperspectral remote sensing image based on artificial immune network.
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2014年第3期289-296,共8页
Journal of Infrared and Millimeter Waves
基金
国家自然科学基金(61271353)
安徽省自然科学基金项目(10040606Q61)~~
关键词
高光谱图像
人工免疫网络
抗体
非线性映射
核空间
hyperspectral imagery, artificial immune network, antibody, nonlinear mapping, kernel space
