Semisupervised Hyperspectral Band Selection Via Spectral-Spatial Hypergraph Model
Semisupervised Hyperspectral Band Selection Via Spectral-Spatial Hypergraph Model
复制标题
通过光谱空间超图模型进行半监督高光谱波段选择
DOI：
10.1109/jstars.2015.2443047
复制
发表时间：
2015
影响因子：
5.5
通讯作者：
Zhou Jun
中科院分区：
文献类型：
--
作者：
Bai Xiao;Guo Zhouxiao;Wang Yanyang;Zhang Zhihong;Zhou Jun
波段选择是实现高光谱图像有效分类的重要步骤。传统的有监督的波段选择方法常常受到训练样本不足的困扰。为了解决这个问题，我们提出了一个半监督波段选择方法，允许标记和未标记的高光谱像素的贡献。该方法首先从所有高光谱样本中建立超图模型来度量像素之间的相似性。我们表明超图可以在光谱域和空间域中捕捉像素之间的关系。在第二步中，引入半监督学习方法来将类标签传播到未标记的样本。然后，一个线性回归模型与组稀疏约束用于波段选择。最后，选择波段的高光谱像素用于训练支持向量机（SVM）分类器。该方法在三个基准数据集上进行了测试。实验结果表明，该方法优于其他几种波段选择方法。
Band selection is an essential step toward effective and efficient hyperspectral image classification. Traditional supervised band selection methods are often hindered by the problem of lacking enough training samples. To address this problem, we propose a semisupervised band selection method that allows contribution from both labeled and unlabeled hyperspectral pixels. This method first builds a hypergraph model from all hyperspectral samples to measure the similarity among pixels. We show that hypergraph can capture relationship among pixels in both spectral and spatial domain. In the second step, a semisupervised learning method is introduced to propagate class labels to unlabeled samples. Then a linear regression model with group sparsity constraint is used for band selection. Finally, hyperspectral pixels with selected bands are used to train a support vector machine (SVM) classifier. The proposed method is tested on three benchmark datasets. Experimental results demonstrate its advantages over several other band selection methods.