Spectral feature extraction of hyperspectral remote sensing images based on kernel class pair-weighted criterion

LIU Jing; LI Qing-yan; LIU Yi

doi:10.37188/OPE.20212906.1397

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Spectral feature extraction of hyperspectral remote sensing images based on kernel class pair-weighted criterion

Information Sciences | 更新时间：2026-09-12

- Spectral feature extraction of hyperspectral remote sensing images based on kernel class pair-weighted criterion
- Optics and Precision Engineering Vol. 29, Issue 6, Pages: 1397-1405(2021)
- 作者机构：
  
  1.西安邮电大学电子工程学院，陕西西安 710121
  2.重庆邮电大学计算机科学与技术学院，重庆 400065
  3.西安电子科技大学电子工程学院，陕西西安 710071
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20212906.1397
  CLC： TP391
- Received：06 July 2020，
  
  Revised：2020-09-05，
  
  Published：15 June 2021
- 稿件说明：
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刘敬,李青妍,刘逸.基于核加权类对准则的高光谱影像特征提取[J].光学精密工程,2021,29(06):1397-1405.

LIU Jing,LI Qing-yan,LIU Yi.Spectral feature extraction of hyperspectral remote sensing images based on kernel class pair-weighted criterion[J].Optics and Precision Engineering,2021,29(06):1397-1405.
刘敬,李青妍,刘逸.基于核加权类对准则的高光谱影像特征提取[J].光学精密工程,2021,29(06):1397-1405. DOI： 10.37188/OPE.20212906.1397.

LIU Jing,LI Qing-yan,LIU Yi.Spectral feature extraction of hyperspectral remote sensing images based on kernel class pair-weighted criterion[J].Optics and Precision Engineering,2021,29(06):1397-1405. DOI： 10.37188/OPE.20212906.1397.

摘要

针对高光谱遥感影像中相似光谱的不同地物与野类同时存在时，提取有效的非线性可分性特征的问题，提出一种核加权类对准则。首先，推导出类对形式的核线性判别分析准则，即核类对准则，将核类间和类内散布矩阵均表示为类对形式。然后，提出核加权类对准则，依据核空间中各类对的可分性分别对各类对的核类间和类内散布矩阵进行加权，使得各类对的可分性均衡地保留在特征子空间中。采用K近邻分类器和最小距离分类器评估特征提取的效果。基于两个实测高光谱遥感影像的实验结果均表明：相比原空间法、核线性判别分析方法和kernel weighted pairwise Fisher准则，所提核加权类对准则在降维的同时，通过提高可分性小的类对的识别率来提高整体地物识别率。

Abstract

To extract efficient nonlinear discriminant features when foreign objects， with similar spectra and outlier classes， are present in hyperspectral remote sensing images （HRSIs）， a kernel class pair-weighted （KCP-weighted） criterion is proposed. First， we derive a class pair form of the kernel linear discriminant analysis （KLDA） criterion， viz. the kernel class pair （KCP） criterion， in which the kernel-between-class and kernel-within-class scatter matrices are both expressed in the form of class pairs. Then， the KCP-weighted criterion is proposed to weight the kernel-between-class and kernel-within-class scatter matrices of each class pair according to their separability in a kernel space. The KCP-weighted criterion can ensure that the separabilities of class pairs are balanced in the KCP-weighted feature subspace. Finally， the K-nearest neighbor and minimum distance classifiers are used to evaluate the feature extraction performance. Experimental results of two real HRSIs show that， compared with the original space and KLDA methods as well as the kernel weighted pairwise Fisher criterion， the presented KCP-weighted criterion can effectively improve the overall terrain classification rate while reducing the dimensionality of the data.

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references

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