CAJ | 학술논문

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。
본문제출일충량분류기융합적고광보공보연합분류방법，수선이용자공간다항식라집회귀재도상적특정자공간중분류，득도만개솔도；근거만개솔장매개상원분지개솔최대적량개최가신유별，병재원시공간중구건최가신유별자전，이용희소해혼대매개상원재최가신유별자전하진행희소표시，득도희소개솔도；최후장만개솔도화희소개솔도선성융합，병이용변연보지적마이가부정칙항알굴도상공간신식，득도구유변연보지적공보분류모형。실험표명，제출적량분류기융합방법즉사재훈련양본교소시야비현유방법득도경호적분류결과。
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 o-riginal HSI.Finally,the full probability and sparse probability are fused linearly and the spatial information is exploit by an edge pre-serving 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.