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一种新的两分类器融合的空谱联合高光谱分类方法
引用本文:孙乐,吴泽彬,冯灿,刘建军,肖亮,韦志辉. 一种新的两分类器融合的空谱联合高光谱分类方法[J]. 电子学报, 2015, 43(11): 2210-2217. DOI: 10.3969/j.issn.0372-2112.2015.11.011
作者姓名:孙乐  吴泽彬  冯灿  刘建军  肖亮  韦志辉
作者单位: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
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.

关 键 词:高光谱分类  子空间逻辑回归  稀疏解混  多分类器  马尔可夫正则项  
收稿时间:2014-02-27

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. 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
Authors:SUN Le  WU Ze-bin  FENG Can  LIU Jian-jun  XIAO Liang  WEI Zhi-hui
Affiliation:1. Jiangsu Engineering Center of Network Monitoring, Nanjing, Jiangsu 210044, China;2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China;3. Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China;4. Software Center of China North Industries Group Corporation, Nanjing, Jiangsu 211153, China
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.
Keywords:hyperspectral classification  subspace multinomial logistic regression  sparse unmixing  multiple classifier  MRF regularizer  
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