Classification algorithm of hyperspectral images based on kernel entropy analysis

Ying Wang; Lei Guo; Nan Liang

Classification algorithm of hyperspectral images based on kernel entropy analysis

Ying Wang
, Lei Guo
, Nan Liang

自动化学院

Northwestern Polytechnical University Xian

科研成果: 期刊稿件 › 文章 › 同行评审

2 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	1597-1601
页数	5
期刊	Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
卷	42
期	6
出版状态	已出版 - 11月 2012

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引用此

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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 = "Hyperspectral image, Image classification, Information processing, Kernel entropy component analysis, Renyi entropy",

author = "Ying Wang and Lei Guo and Nan Liang",

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AU - Wang, Ying

AU - Guo, Lei

AU - Liang, Nan

PY - 2012/11

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N2 - 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.

AB - 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.

KW - Hyperspectral image

KW - Image classification

KW - Information processing

KW - Kernel entropy component analysis

KW - Renyi entropy

UR - https://www.scopus.com/pages/publications/84870746786

M3 - 文章

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EP - 1601

JO - Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)

JF - Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)

IS - 6

ER -

Classification algorithm of hyperspectral images based on kernel entropy analysis

摘要

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