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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Uniformity-Based Superpixel Segmentation of Hyperspectral Images
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Uniformity-Based Superpixel Segmentation of Hyperspectral Images

机译：基于均匀度的高光谱图像超像素分割

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摘要

Superpixel segmentation algorithms attempt to group contiguous image pixels which are in homogeneous regions into segments (superpixels). Superpixel segmentation maps have proven successful in improving the performance of unmixing algorithms on hyperspectral images. For hyperspectral images (HSIs), segment members must contain spectrally similar pixels, a requirement we refer to as segment uniformity. Existing superpixel segmentation algorithms which have been applied to HSIs provide no guarantees on the uniformity inside segments. In the absence of such guarantees, the only viable option is to make the segments small enough that uniformity is always ensured; this leads to an oversegmentation of the image. An accurate uniformity measure would lead to a more accurate segmentation. We propose a graph-based agglomerative approach that enforces segment uniformity by setting a threshold for maximum variability inside segments. The threshold is computed by a statistical analysis of the within-class and between-class spectral divergences of several mineral families of interest. We show that the proposed algorithm can be used to generate parsimonious segmentations and facilitate the computation of accurate mineralogical summaries for several simulated and real HSIs of terrestrial and planetary geological surfaces.
机译：超像素分割算法尝试将均匀区域中的连续图像像素分组为段（超像素）。事实证明，超像素分割图可以成功地改善高光谱图像上的混合算法的性能。对于高光谱图像（HSI），分段成员必须包含光谱相似的像素，这就是我们所说的分段均匀性。现有的应用于HSI的超像素分割算法无法保证片段内部的均匀性。在没有此类保证的情况下，唯一可行的选择是使段足够小，以确保始终保持一致性。这会导致图像过度分割。准确的均匀性度量将导致更准确的细分。我们提出了一种基于图的聚集方法，该方法通过设置段内最大可变性的阈值来强制段统一。该阈值是通过对几种感兴趣的矿物家族的类内和类间光谱散度的统计分析来计算的。我们表明，所提出的算法可用于生成简约分割，并简化了对地球和行星地质表面的多个模拟和真实HSI的精确矿物学摘要的计算。

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