Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images

Du Weibao; Qiang Wenwen; Lv Meng; Hou Qiuling; Zhen Ling; Jing Ling

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Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images

机译：基于超图嵌入的高光谱图像半监督降维

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摘要

Dimension reduction (DR) is an efficient and effective preprocessing step of hyperspectral images (HSIs) classification. Graph embedding is a frequently used model for DR, which preserves some geometric or statistical properties of original data set. The embedding using simple graph only considers the relationship between two data points, while in real-world application, the complex relationship between several data points is more important. To overcome this problem, we present a linear semi-supervised DR method based on hypergraph embedding (SHGE) which is an improvement of semi-supervised graph learning (SEGL). The proposed SHGE method aims to find a projection matrix through building a semi-supervised hypergraph which can preserve the complex relationship of the data and the class discrimination for DR. Experimental results demonstrate that our method achieves better performance than some existing DR methods for HSIs classification and is time saving compared with the existed method SEGL which used simple graph.

机译：降维（DR）是高光谱图像（HSI）分类的一种高效有效的预处理步骤。图嵌入是DR常用的模型，它保留了原始数据集的某些几何或统计属性。使用简单图进行嵌入仅考虑两个数据点之间的关系，而在实际应用中，多个数据点之间的复杂关系更为重要。为了克服这个问题，我们提出了一种基于超图嵌入（SHGE）的线性半监督DR方法，它是对半监督图学习（SEGL）的改进。提出的SHGE方法旨在通过构建半监督超图来找到投影矩阵，该投影矩阵可以保留数据的复杂关系和DR的类区分。实验结果表明，与现有的简单图形SEGL方法相比，该方法具有优于现有DR方法进行HSI分类的性能，并且节省了时间。

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来源
《International journal of remote sensing》 |2018年第6期|1696-1712|共17页
作者
Du Weibao; Qiang Wenwen; Lv Meng; Hou Qiuling; Zhen Ling; Jing Ling;
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作者单位

China Agr Univ, Coll Sci, Beijing 100083, Peoples R China;

China Agr Univ, Coll Sci, Beijing 100083, Peoples R China;

China Agr Univ, Coll Sci, Beijing 100083, Peoples R China;

China Agr Univ, Coll Sci, Beijing 100083, Peoples R China;

China Agr Univ, Coll Sci, Beijing 100083, Peoples R China;

China Agr Univ, Coll Sci, Beijing 100083, Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
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专利

1. Semi-Supervised Dimensionality Reduction of Hyperspectral Image Based on Sparse Multi-Manifold Learning [J] . Hong Huang, Fulin Luo, Zezhong Ma, Journal of Computer and Communications . 2015,第11期

机译：基于稀疏多流形学习的高光谱图像半监督降维
2. Sparse dimensionality reduction of hyperspectral image based on semi-supervised local Fisher discriminant analysis [J] . Zhenfeng Shao, Lei Zhang International journal of applied earth observation and geoinformation . 2014,第Null期

机译：基于半监督局部Fisher判别分析的高光谱图像稀疏度降低
3. Patch Tensor-Based Multigraph Embedding Framework for Dimensionality Reduction of Hyperspectral Images [J] . Deng Yang-Jun, Li Heng-Chao, Song Xin, IEEE Transactions on Geoscience and Remote Sensing . 2020,第3期

机译：基于贴片的多标签嵌入框架，用于高光谱图像的维数减少
4. Class-Probability Based Semi-Supervised Dimensionality Reduction for Hyperspectral Images [C] . Lu Liang, Yi Xia, Lina Xun, IEEE International Conference on Software Engineering and Service Science . 2018

机译：基于类概率的高光谱图像半监督降维
5. A comparison on methods for dimensionality reduction of hyperspectral images. [D] . Arzuaga Cruz, Emmanuel. 2003

机译：高光谱图像降维方法的比较。
6. Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding [O] . Hong Liu, Kewen Xia, Tiejun Li, 2020

机译：基于改进的空间光谱重量歧管嵌入的高光谱图像的维数减少
7. Semi-Supervised Dimensionality Reduction of Hyperspectral Image Based on Sparse Multi-Manifold Learning [O] . Hong Huang, Fulin Luo, Zezhong Ma, 2015

机译：基于稀疏多流形学习的半光谱图像的半监督减少

Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images

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