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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Unsupervised Satellite Image Classification Using Markov Field Topic Model
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Unsupervised Satellite Image Classification Using Markov Field Topic Model

机译：马尔可夫场主题模型的无监督卫星图像分类

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摘要

Recently, the combination of topic models and random fields has been frequently and successfully applied to image classification due to their complementary effect. However, the number of classes is usually needed to be assigned manually. This letter presents an efficient unsupervised semantic classification method for high-resolution satellite images. We add label cost, which can penalize a solution based on a set of labels that appear in it by optimization of energy, to the random fields of latent topics, and an iterative algorithm is thereby proposed to make the number of classes finally be converged to an appropriate level. Compared with other mentioned classification algorithms, our method not only can obtain accurate semantic segmentation results by larger scale structures but also can automatically assign the number of segments. The experimental results on several scenes have demonstrated its effectiveness and robustness.
机译：近年来，由于主题模型和随机字段的互补作用，它们的组合已被频繁且成功地应用于图像分类。但是，通常需要手动分配类的数量。这封信为高分辨率卫星图像提供了一种有效的无监督语义分类方法。我们将标签成本添加到潜在主题的随机字段中，该标签成本可以通过优化能量来惩罚基于其中出现的一组标签的解决方案，从而提出一种迭代算法以最终将类的数量收敛为适当的水平。与其他提到的分类算法相比，我们的方法不仅可以通过较大规模的结构获得准确的语义分割结果，而且可以自动分配分割数。在几个场景上的实验结果证明了其有效性和鲁棒性。

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