.png){kind=icon}
返回
Learning With Hypergraph for Hyperspectral Image Feature Extraction
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2015.2419713.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
It is known that hyperspectral image (HSI) classification is a high-dimension low-sample-size problem. To ease this problem, one natural idea is to take the feature extraction as a preprocessing. A graph embedding model is a classic family of feature extraction methods, which preserves certain statistical or geometric properties of the data set. However, the graph embedding model considers only the pairwise relationship between two vertices, which cannot represent the complex relationships of the data. Utilizing the spatial structure of HSI, in this letter, we propose a spatial hypergraph embedding model for feature extraction. Experimental results demonstrate that our method outperforms many existing feature extract methods for HSI classification.
Keyword:
Classification
feature extraction
hypergraph embedding
spatial neighborhood
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
引用论文
Advances in Spectral-Spatial Classification of Hyperspectral Images高光谱图像光谱-空间分类研究进展.png)
PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images用于高光谱图像特征提取的半监督局部判别分析
PREAI
分享收藏
查看更多内容