[1]龙咏红.面向高光谱图像的高斯-稀疏子空间聚类算法[J].佛山科学技术学院学报(自然科学版),2020,(06):039-47.
 LONG Yong-hong.Gaussian-sparse subspace clustering algorithm for hyper spectral imagery[J].JOURNAL OF FOSHAN UNIVERSITY NATUAL SCIENCE EDITION,2020,(06):039-47.
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面向高光谱图像的高斯-稀疏子空间聚类算法
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《佛山科学技术学院学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年06期
页码:
039-47
栏目:
信息科学
出版日期:
2020-11-30

文章信息/Info

Title:
Gaussian-sparse subspace clustering algorithm for hyper spectral imagery
文章编号:
1008-0171(2020)06-0039-09
作者:
龙咏红
(广东工业大学 应用数学学院,广东 广州 510520)
Author(s):
LONG Yong-hong
(School of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520, China)
关键词:
高光谱图像稀疏子空间聚类高斯相似度空间信息
Keywords:
hyper spectral images sparse subspace clustering Gaussian-similarity spatial information
分类号:
TP751;TP391.41
文献标志码:
A
摘要:
稀疏子空间聚类算法聚类图像是基于谱聚类实现的,谱聚类的关键是构造图的相似度矩阵,在稀疏子空间算法的基础上,提出一种利用稀疏系数矩阵与经过 PCA 处理后像素的高斯相似度来构造图的相似度矩阵的聚类算法。 实验结果表明,提出的聚类算法充分提高了高光谱图像的聚类精度。
Abstract:
The sparse subspace clustering algorithm is based on spectral clustering. The key of spectral clustering is to construct the similarity matrix of the pixels. Based on the sparse subspace algorithm, a method is proposed which uses sparse coefficient matrix and Gaussian-similarity of hyper spectral pixels that are processed through PCA. Experimental results show that the proposed clustering algorithm fully improves the clustering accuracy of hyper spectral images.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-12-19作者简介:龙咏红(1995-),女,江西吉安人,广东工业大学硕士研究生。
更新日期/Last Update: 2020-11-26