Semi-supervised hyperspectral image segmentation
Semi-supervised hyperspectral image segmentation
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DOI：
10.1109/whispers.2009.5289082
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Jun Li;J. Bioucas-Dias;A. Plaza
中科院分区：
文献类型：
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作者：
Jun Li;J. Bioucas-Dias;A. Plaza
本文提出了一种新的半监督分割算法，适用于高维数据，其中高光谱图像是一个例子。该算法实现了两个主要步骤：（a）半监督学习，用于推断类分布，其次是（B）分割，通过推断标签从后验密度建立在学习类分布和马尔可夫随机场。类分布是用多项逻辑回归建模的，其中回归量是使用标记样本和未标记样本（通过基于图形的技术）来学习的。标签上的先验是一个多水平logistic模型。最大后验分割采用基于α-Expansion min-cut的整数优化算法。我们给出的实验证据表明，空间先验极大地提高了分割性能，相对于半监督分类器。仿真和真实的数据验证了该方法的有效性。
This paper presents a new semi-supervised segmentation algorithm, suited to high dimensional data, of which hyperspectral images are an example. The algorithm implements two main steps: (a) semisupervised learning, used to infer the class distributions, followed by (b) segmentation, by inferring the labels from a posterior density built on the learned class distributions and on a Markov random field. The class distributions are modeled with a multinomial logistic regression, where the regressors are learned using both labeled and, through a graph-based technique, unlabeled samples. The prior on the labels is a multi-level logistic model. The maximum a posterior segmentation is computed by the α-Expansion min-cut based integer optimization algorithm. We give experimental evidence that the spatial prior greatly improves the segmentation performance, with respect to that of a semi-supervised classifier. The effectiveness of the proposed method is demonstrated with simulated and real data.