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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Nonparametric Coupled Bayesian Dictionary and Classifier Learning for Hyperspectral Classification
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Nonparametric Coupled Bayesian Dictionary and Classifier Learning for Hyperspectral Classification

机译：高光谱分类的非参数贝叶斯耦合字典和分类器学习

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摘要

We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size—the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.
机译：我们提出了一种原则性的方法，用于学习沿线性分类器进行高光谱分类的判别词典。我们的方法将高斯过程先验放在字典上，以说明自然光谱的相对平滑度，而分类器参数是从多元高斯抽样的。我们采用两个Beta-Bernoulli过程来共同推断字典和分类器。这些过程在相同的伯努利分布集下耦合。在我们的方法中，这些分布表示字典原子在表示特定于类的训练谱中的使用频率，这也使字典具有区分性。由于字典和分类器之间的耦合，用于表示不同类的原子的流行度被编码到分类器中。通过解决同时稀疏优化问题，这有助于预测首先在字典上表示的测试光谱的类别标签。通过将结果表示输入分类器来预测光谱的标记。我们的方法利用非参数贝叶斯框架自动推断字典大小，这是判别字典学习中的关键参数。此外，它还具有自适应地学习字典原子和类别标签之间的关联的理想特性。我们使用Gibbs采样来推断所提出模型下字典和分类器的后验概率分布，为此，我们得出了解析表达式。为了确定我们方法的有效性，我们在基准高光谱图像上对其进行了测试。将分类性能与基于最新词典学习的分类方法进行比较。

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