Hyperspectral Image Classification Based on Feature Subspace Evaluation and Multiple Classifier Fusion

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Hyperspectral Image Classification Based on Feature Subspace Evaluation and Multiple Classifier Fusion

更新时间：2025-07-09

- Hyperspectral Image Classification Based on Feature Subspace Evaluation and Multiple Classifier Fusion
- Vol. 44, Issue 8, Pages: 20-24(2010)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Online First：10 August 2010，
  
  Published：2010
- 稿件说明：
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Hyperspectral Image Classification Based on Feature Subspace Evaluation and Multiple Classifier Fusion[J]. 2010, 44(8): 20-24.
DOI：

Hyperspectral Image Classification Based on Feature Subspace Evaluation and Multiple Classifier Fusion[J]. 2010, 44(8): 20-24. DOI：

摘要

为应对高光谱图像分类中的特征高维度问题

提出一种基于多分类器融合的高光谱图像分类方法.利用高光谱数据相邻波段的高相关性

通过自适应子空间分解产生多个特征子空间

进而训练生成子分类器; 利用ReliefF-S算法

对各特征子空间进行评价并生成各子分类器的权重

最终通过加权表决融合实现分类决策.实验表明

所提方法可有效规避高维特征问题并提升分类性能.

Abstract

A novel approach for hyperspectral image classification is proposed based on fusion of multiple classifiers to deal with the high dimension in applications of hyperspectral image classification. High correlation of neighboring bands of hyperspectral image data is used to generate feature subsets through adaptive subspace decomposition. A modified ReliefF algorithm(ReliefF-S)is proposed to evaluate feature subsets and to generate their corresponding weight values. Member classifiers are trained based on resulting feature subspaces and their weighted majority voting

and then the fusion of multiple classifiers is accomplished. Experimental results show that the proposed approach reduces the dimension of features effectively

and improves the classification performance.

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references

PLAZA A, BENEDIKTSSON J A, BOARDMAN J W. Recent advances in techniques for hyperspectral image processing [J]. Remote Sensing of Environment 2009, 113: S110-S122.

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LANDGREBE D. On information extraction principles for hyperspectral data: a white paper[R]. West Lafayette, Indianan, USA: Purdue University. School of Electrical and Computer Engineering, 1997.

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孙亮. 高维遥感数据融合与分类的知识发现方法研究[D]. 西安: 西安交通大学电子与信息工程学院,2006.

韩德强. 基于证据推理的多源分类融合理论与方法研究[D].西安: 西安交通大学电子与信息工程学院,2008.

ITT. ENVI tutorial: vegetation hyperspectral analysis [EB/OL].[2009-11-06].http:∥www.ittvis.com/portals/o/tutorials/envi/Vegetation-Analysis.pdf.

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