Superpixels for Spatially Reinforced Bayesian Classification of Hyperspectral Images
Superpixels for Spatially Reinforced Bayesian Classification of Hyperspectral Images
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DOI：
10.1109/lgrs.2014.2380313
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发表时间：
2015-01
影响因子：
4.8
通讯作者：
T. Priya;S. Prasad;Hao Wu
中科院分区：
文献类型：
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作者：
T. Priya;S. Prasad;Hao Wu
这封信提出了一种基于超像素的新型高光谱图像分析方法，该方法利用光谱相似的连续像素内的空间上下文来进行稳健的高光谱分类。所提出的方法需要两个关键步骤——首先，作为预处理步骤，我们通过基于图的分割计算分组（超像素），然后使用决策融合方法进行对象级分类，该方法合并来自“每像素”贝叶斯分类器集合的每像素结果。所提出的方法提供了一种利用空间上下文信息的稳健方法。超像素中的每个像素都使用统计贝叶斯分类独立进行分类，并且合并决策以获得每个超像素的唯一类标签。高光谱图像的实验结果表明，即使使用非常有限的训练数据，所提出的方法也始终提供鲁棒的分类框架。
This letter presents a novel superpixel-based approach to hyperspectral image analysis which exploits spatial context within spectrally similar contiguous pixels for robust hyperspectral classification. The proposed approach entails two key steps-first, as a preprocessing step, we compute groupings (superpixels) through graph-based segmentation, following which an object-level classification is undertaken using a decision fusion approach that merges per-pixel outcomes from an ensemble of “per-pixel” Bayesian classifiers. The proposed method provides a robust way to exploit spatial contextual information. Every pixel in a superpixel is classified using statistical Bayesian classification independently, and the decisions are merged to obtain a unique class label for each superpixel. Experimental results with hyperspectral imagery indicate that the proposed method consistently provides a robust classification framework, even when using very limited training data.