SAR-Based Terrain Classification Using Weakly Supervised Hierarchical Markov Aspect Models
SAR-Based Terrain Classification Using Weakly Supervised Hierarchical Markov Aspect Models
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DOI：
10.1109/tip.2012.2199127
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发表时间：
2012-09
影响因子：
10.6
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中科院分区：
文献类型：
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我们介绍了分层马尔可夫特征模型(HMAM)，这是一种计算效率很高的图形模型，用于密集地标记大型遥感图像的底层地形类。HMAM通过结合四叉树表示和特征模型的优点来有效地解决局部歧义-前者结合了多尺度视觉特征和分层平滑来提供改进的局部标签一致性，而后者通过将标签聚焦于与更广泛的局部图像上下文最相关的类别来锐化标签。完整的HMAM模型采用图像块上的局部分层马尔可夫四叉树的网格，并通过在四叉树森林的每一层的较大局部图像瓦片上结合概率潜在语义分析方面模型来对其进行扩充。对于每个层次和块，提取词袋视觉特征，并给定这些特征，即来自四叉树的父子转移概率和来自瓦片级特征模型的标签概率，有效的向前-向后推理通过允许获得每个块的类别标签的局部后验。然后使用变分期望最大化从像素级或平铺关键字级标签训练完整的模型。在具有像素级地面真实的完整TerraSAR-X合成孔径雷达地形图上的实验表明，HMAM既准确又有效，在训练和测试复杂度仅略有增加的情况下，提供了比可比的单尺度方位向模型更好的结果。关键字级别的训练大大降低了提供训练数据的成本，而与像素级别的训练相比，精度损失很小。
We introduce the hierarchical Markov aspect model (HMAM), a computationally efficient graphical model for densely labeling large remote sensing images with their underlying terrain classes. HMAM resolves local ambiguities efficiently by combining the benefits of quadtree representations and aspect models—the former incorporate multiscale visual features and hierarchical smoothing to provide improved local label consistency, while the latter sharpen the labelings by focusing them on the classes that are most relevant for the broader local image context. The full HMAM model takes a grid of local hierarchical Markov quadtrees over image patches and augments it by incorporating a probabilistic latent semantic analysis aspect model over a larger local image tile at each level of the quadtree forest. Bag-of-word visual features are extracted for each level and patch, and given these, the parent–child transition probabilities from the quadtree and the label probabilities from the tile-level aspect models, an efficient forwards–backwards inference pass allows local posteriors for the class labels to be obtained for each patch. Variational expectation-maximization is then used to train the complete model from either pixel-level or tile-keyword-level labelings. Experiments on a complete TerraSAR-X synthetic aperture radar terrain map with pixel-level ground truth show that HMAM is both accurate and efficient, providing significantly better results than comparable single-scale aspect models with only a modest increase in training and test complexity. Keyword-level training greatly reduces the cost of providing training data with little loss of accuracy relative to pixel-level training.