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首页> 外文期刊>Journal of Applied Remote Sensing >Pixel classification of large-size hyperspectral images by affinity propagation
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Pixel classification of large-size hyperspectral images by affinity propagation

机译：通过亲和力传播对大尺寸高光谱图像进行像素分类

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Affinity propagation (AP) is now among the most used methods for unsupervised classification. However, it has two major drawbacks: (1) the number of classes (NCs) is over-estimated when the preference parameter value is initialized as the median value of the similarity matrix; and (2) the partitioning of large-size hyperspectral images is hampered by its quadratic computational complexity. To overcome these two drawbacks, we propose an approach which consists of reducing the number of pixels to be classified before the application of AP. To reduce the number of pixels, the hyperspectral image is divided into blocks, and the reduction step is then independently applied within each block. This step requires less memory storage, since the calculation of the full similarity matrix is no longer required. AP is applied on the new set of pixels, which is then set up from the representatives of each previously formed cluster and nonaggregated pixels. To correctly estimate the NCs, we introduced a bisection method which aims to assess intermediate classification results using a criterion based on pixel interclass variance. The application of this approach on hyperspectral images shows that our results are efficient and independent of the block size. (C) The Authors.
机译：亲和传播（AP）现在是用于无监督分类的最常用方法之一。但是，它有两个主要缺点：（1）当将偏好参数值初始化为相似度矩阵的中值时，类别（NC）的数量被高估了; （2）大型高光谱图像的分割因其二次计算复杂性而受到阻碍。为了克服这两个缺点，我们提出一种方法，该方法包括在应用AP之前减少要分类的像素数量。为了减少像素数量，将高光谱图像划分为多个块，然后在每个块内独立应用缩小步骤。由于不再需要计算完全相似矩阵，因此此步骤需要较少的存储器存储。将AP应用于新的像素集，然后从每个先前形成的群集和未聚集像素的代表中设置AP。为了正确估计NC，我们引入了一种二等分方法，旨在使用基于像素类间差异的标准评估中间分类结果。这种方法在高光谱图像上的应用表明，我们的结果是有效的，并且与块大小无关。 （C）作者。

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