[1]黄晓伟,杭仁龙,孙玉宝,等.基于稀疏图正则矩阵判别分析的高光谱图像分类[J].南京师范大学学报(自然科学版),2019,42(01):51.[doi:10.3969/j.issn.1001-4616.2019.01.009]
　Huang Xiaowei,Hang Renlong,Sun Yubao,et al.Sparse Graph Regularized Matrix Discriminant Analysis forHyperspectral Image Classification[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(01):51.[doi:10.3969/j.issn.1001-4616.2019.01.009]
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基于稀疏图正则矩阵判别分析的高光谱图像分类()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年01期
页码:
51
栏目:
·人工智能算法与应用专栏·
出版日期:
2019-03-20

文章信息/Info

Title:
Sparse Graph Regularized Matrix Discriminant Analysis forHyperspectral Image Classification
文章编号:
1001-4616(2019)01-0051-08
作者:
黄晓伟; 杭仁龙; 孙玉宝; 刘青山
江苏省大数据分析技术重点实验室,南京信息工程大学信息与控制学院,江苏 南京 210044
Author(s):
Huang Xiaowei; Hang Renlong; Sun Yubao; Liu Qingshan
Jiangsu Key Laboratory of Big Data Analysis Technology,School of Information and Control,Nanjing University of Information Science and Technology,Nanjing 210044,China
关键词:
高光谱图像分类; 谱-空特征融合; 矩阵判别分析; 稀疏图正则
Keywords:
hyperspectral image classification; spatial-spectral feature fusion; matrix-based discriminant analysis(MDA); sparse graph regularization
分类号:
TP751
DOI:
10.3969/j.issn.1001-4616.2019.01.009
文献标志码:
A
摘要:
光谱和空间信息的联合使用是高光谱图像分类领域的研究热点之一. 本文在已有的矩阵判别分析(MDA)模型的基础上,提出了一种基于稀疏图正则的改进模型. 在有效融合高光谱图像光谱-空间信息的同时,能充分挖掘无标签样本的信息,从而提升了模型的分类性能. 为了验证本文算法的有效性,在两个高光谱数据集上,与多种方法进行了对比. 实验结果表明,本文提出的算法优于其他同类算法.
Abstract:
In the field of hyperspectral image classification,the incorporation of spectral information and spatial information is one of the hot research topics. In this paper,a modified matrix-based discriminant analysis(MDA)model is proposed based on sparse graph regularization. The proposed model can not only combine the spectral information and spatial information effectively,but also sufficiently explore the wealth information from unlabeled samples,thus improving the classification performance. In order to verify the effectiveness of the proposed method,experiments have been conducted on two widely used hyperspectral images. The experimental results show that the performance of our method is superior as compared to other methods.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-09-18.
基金项目:国家自然科学基金(61672292)、江苏省高校自然科学研究面上项目(18KJB520032)、江苏省青年基金项目(BK20180786).
通讯联系人:刘青山,博士,教授,博士生导师,研究方向:模式识别与计算机视觉. E-mail:qsliu@nuist.edu.cn
更新日期/Last Update: 2019-03-30