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基于核熵成分分析的高光谱遥感图像分类算法 被引量：2

Classification algorithm of hyperspectral images based on kernel entropy analysis
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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。 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.
作者 王瀛 郭雷 梁楠
机构地区 西北工业大学自动化学院
出处 《吉林大学学报（工学版）》 EI CAS CSCD 北大核心 2012年第6期1597-1601,共5页 Journal of Jilin University:Engineering and Technology Edition
基金 国家自然科学基金项目(60802084)
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 information processing hyperspectral image Renyi entropy image classification kernel entropy component analysis
分类号 TN911.73 [电子电信—通信与信息系统]
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参考文献10

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同被引文献20

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吉林大学学报（工学版）
2012年 第6期

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