Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation
Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation
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DOI：
10.1016/j.isprsjprs.2014.08.016
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发表时间：
2014-11
影响因子：
12.7
通讯作者：
Liguo Wang;Siyuan Hao;Qunming Wang;Ying Wang
中科院分区：
文献类型：
--
作者：
Liguo Wang;Siyuan Hao;Qunming Wang;Ying Wang
基于图的分类算法在半监督分类中受到越来越多的关注。然而，该图不能完全代表数据固有的空间分布。本文提出了一种基于空间光谱标签传播的新分类方法，用于高光谱图像的半监督分类。空间信息的利用有两个方面：一方面，将二维Gabor滤波器提取的空间特征与光谱特征进行叠加；另一方面，用于构建图的高斯函数的宽度是通过自适应方法确定的。随后，从标记样本的空间邻居中选择未标记样本，并基于空间平滑度构建空间图。最后，利用空间谱图将标签从标记样本传播到未标记样本，以更新基本分类器（例如支持向量机，SVM）的训练集。在四个高光谱数据集上的实验表明，所提出的基于支持向量机（SS-LPSVM）的空间光谱标签传播可以在半监督学习框架下有效地表示空间信息，并且始终比标准支持向量机、拉普拉斯支持向量机（LapSVM）、传导支持向量机（TSVM）和空间上下文半监督支持向量机（SCS3VM）产生更高的分类精度。
Graph-based classification algorithms have gained increasing attention in semi-supervised classification. Nevertheless, the graph cannot fully represent the inherent spatial distribution of the data. In this paper, a new classification methodology based on the spatial-spectral Label Propagation is proposed for semi-supervised classification of hyperspectral imagery. The spatial information was used in two aspects: on the one hand, the spatial features extracted by a 2-D Gabor filter were stacked with spectral features; on the other hand, the width of the Gaussian function, which was used to construct graph, was determined with an adaptive method. Subsequently, the unlabeled samples from the spatial neighbors of the labeled samples were selected and the spatial graph was constructed based on spatial smoothness. Finally, labels were propagated from labeled samples to unlabeled samples with spatial-spectral graph to update the training set for a basic classifier (e.g., Support Vector Machine, SVM). Experiments on four hyperspectral datasets show that the proposed Spatial-Spectral Label Propagation based on the SVM (SS-LPSVM) can effectively represent the spatial information in the framework of semi-supervised learning and consistently produces greater classification accuracy than the standard SVM, the Laplacian Support Vector Machine (LapSVM), Transductive Support Vector Machine (TSVM) and the Spatial-Contextual Semi-Supervised Support Vector Machine (SCS3VM).