Semi-supervised contextual classification and unmixing of hyperspectral data based on mixture distributions
Semi-supervised contextual classification and unmixing of hyperspectral data based on mixture distributions
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DOI：
10.1109/igarss.2009.5418071
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发表时间：
2009-07
期刊：
影响因子：
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通讯作者：
R. Nishii;Tomohiko Ozaki;Yoko Sawamura
中科院分区：
文献类型：
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作者：
R. Nishii;Tomohiko Ozaki;Yoko Sawamura
本文研究了小训练数据集下高光谱数据的图像分解问题。提出了一种半监督的高光谱数据上下文解混方法。特征向量和类别分数向量的分布分别采用高斯混合模型和马尔可夫随机场模型。然后，通过EM算法和ICM方法，我们推导出一种半监督的混合分解方法。通过人工和真实的数据集对所提出的方法进行了检验，并显示出良好的性能。
This paper considers image unmixing of hyperspectral data with a small training data set. We propose a semi-supervised contextual unmixing method for hyperspectral data. Gaussian mixture models and a novel MRF (Markov random field) are assumed for distributions of feature vectors and category fraction vectors, respectively. Then, we derive a semi-supervised unmixing method through EM algorithm and ICM method. The proposed method is examined through artificial and real data sets, and shows a excellent performance.