| 基于核熵成分分析的高光谱遥感图像分类算法 |
| |
| 引用本文： | 王瀛,郭雷,梁楠. 基于核熵成分分析的高光谱遥感图像分类算法[J]. 吉林大学学报(工学版), 2012, 42(6): 1597-1601 |
| |
| 作者姓名： | 王瀛 郭雷 梁楠 |
| |
| 作者单位： | 西北工业大学自动化学院,西安,710129 |
| |
| 基金项目： | 国家自然科学基金项目(60802084) |
| |
| 摘 要： | 根据核熵成分分析(KECA)的特点提出了基于凸面几何学概念的样本集选取方法和以特征空间光谱角为相似性度量的C-均值分类算法,并将其用于高光谱遥感图像分类。在HYDICE高光谱数据上的试验表明,本文提出的算法可以有效地提高分类精度。
|
| 关 键 词： | 信息处理技术 高光谱图像 Renyi熵 图像分类 核熵成分分析 |
Classification algorithm of hyperspectral images based on kernel entropy analysis |
| |
| WANG Ying,GUO Lei,LIANG Nan. Classification algorithm of hyperspectral images based on kernel entropy analysis[J]. Journal of Jilin University:Eng and Technol Ed, 2012, 42(6): 1597-1601 |
| |
| Authors: | WANG Ying GUO Lei LIANG Nan |
| |
| Affiliation: | (Institute of Automatic,Northwest Polytechnical University,Xi’an 710129,China) |
| |
| Abstract: | To take advantage of the characteristics of KECA for hyperspectral remote sensing image classification,an approach of sample set selection and C-means classification is proposed.The sample selection is based on convex geometry concepts and C-means classification uses spectral angles as distance metrics in feature space.Experiment results of HYDICE hyperspectral data confirm that the proposed approach can improve classification accuracy effectively. |
| |
| Keywords: | information processing hyperspectral image Renyi entropy image classification kernel entropy component analysis |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
|
