A Novel Two-Classifier Fusion Method for Spectral-Spatial Hyperspectral Classification

SUN Le; WU Ze-bin; FENG Can; LIU Jian-jun; XIAO Liang; WEI Zhi-hui

doi:10.3969/j.issn.0372-2112.2015.11.011

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A Novel Two-Classifier Fusion Method for Spectral-Spatial Hyperspectral Classification

更新时间：2025-07-16

- A Novel Two-Classifier Fusion Method for Spectral-Spatial Hyperspectral Classification
- Acta Electronica Sinica Vol. 43, Issue 11, Pages: 2210-2217(2015)
- 作者机构：
  
  1. 江苏省网络监控工程中心,江苏,南京,210044
  2. 南京信息工程大学计算机与软件学院,江苏,南京,210044
  3. 南京理工大学计算机科学与工程学院,江苏,南京,210094
  4. 北方信息控制集团软件中心,江苏,南京,211153
  5. 江苏省网络监控工程中心,江苏,南京,210044
  6. 南京信息工程大学计算机与软件学院,江苏,南京,210044
  7. 南京理工大学计算机科学与工程学院,江苏,南京,210094
  8. 北方信息控制集团软件中心,江苏,南京,211153
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.11.011
  CLC： TP751
- Published：2015
- 稿件说明：
移动端阅览
SUN Le, WU Ze-bin, FENG Can, et al. A Novel Two-Classifier Fusion Method for Spectral-Spatial Hyperspectral Classification[J]. Acta Electronica Sinica, 2015, 43(11): 2210-2217.
DOI：

SUN Le, WU Ze-bin, FENG Can, et al. A Novel Two-Classifier Fusion Method for Spectral-Spatial Hyperspectral Classification[J]. Acta Electronica Sinica, 2015, 43(11): 2210-2217. DOI： 10.3969/j.issn.0372-2112.2015.11.011.

摘要

本文提出一种两分类器融合的高光谱空谱联合分类方法

首先利用子空间多项式逻辑回归在图像的特征子空间中分类

得到满概率图;根据满概率将每个像元分至概率最大的两个最可信类别

并在原始空间中构建最可信类别字典

利用稀疏解混对每个像元在最可信类别字典下进行稀疏表示

得到稀疏概率图;最后将满概率图和稀疏概率图线性融合

并利用边缘保持的马尔可夫正则项挖掘图像空间信息

得到具有边缘保持的空谱分类模型.实验表明

提出的两分类器融合方法即使在训练样本较少时也比现有方法得到更好的分类结果.

Abstract

This paper presents a new multiple-classifier approach for spectral-spatial classification of hyperspectral images(HSI).Firstly

subspace based multinomial logistic regression(MLRsub) method is used to calculate the full probability of each pixel in the feature space;Secondly

the sub-dictionary is constructed by the training samples of the most two reliable classes

which is determined by the full probability for each pixel.Then

sparse unmixing(SU) is used to calculate the sparse probability in the original HSI.Finally

the full probability and sparse probability are fused linearly and the spatial information is exploit by an edge preserving Markov random field(MRF) regularizer.Experimental results indicate that our proposed multiple-classifier leads to better classification performance than the state-of-the-art methods

even with small training samples.

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Spectral Reweighted Collaborative Sparsity and Total Variation Based Hyperspectral Unmixing Method

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