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Affinity propagation for large size hyperspectral image classification

机译：大尺寸高光谱图像分类的亲和力传播

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摘要

The affinity propagation (AP) is now among the most used methods of unsupervised classification. However, it has two major disadvantages. On the one hand, the algorithm implicitly controls the number of classes from a preference parameter, usually initialized as the median value of the similarity matrix, which often gives over-clustering. On the other hand, when partitioning large size hyperspectral images, its computational complexity is quadratic and seriously hampers its application. To solve these two problems, we propose a method which consists of reducing the number of individuals to be classified before the application of the AP, and to concisely estimate the number of classes. For the reduction of the number of pixels, a pre-classification step that automatically aggregates highly similar pixels is introduced. The hyperspectral image is divided into blocks, and then the reduction step is applied independently within each block. This step requires less memory storage since the calculation of the full similarity matrix is not required. The AP is then applied on the new set of pixels which are then set up from the representatives of each previously formed cluster and non-aggregated individuals. To estimate the number of classes, we introduced a dichotomic method to assess classification results using a criterion based on inter-class variance. The application of this method on various test images has shown that AP results are stable and independent to the choice of the block size. The proposed approach was successfully used to partition large size real datasets (multispectral and hyperspectral images).
机译：亲和力传播（AP）现在是最常用的无监督分类方法之一。但是，它有两个主要缺点。一方面，该算法通过首选项参数隐式控制类的数量，通常将其初始化为相似性矩阵的中值，这通常会导致过度聚类。另一方面，当分割大尺寸高光谱图像时，其计算复杂度是二次的，严重地妨碍了其应用。为了解决这两个问题，我们提出了一种方法，该方法包括在应用AP之前减少要分类的个人的数量，并简洁地估计类别的数量。为了减少像素数量，引入了自动聚合高度相似像素的预分类步骤。高光谱图像被分成多个块，然后在每个块内独立应用缩小步骤。由于不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。然后，将AP应用于新的像素集，然后从每个先前形成的聚类和未聚合的个体的代表中建立该像素。为了估计类的数量，我们引入了一种二分类方法，使用基于类间差异的标准来评估分类结果。该方法在各种测试图像上的应用表明，AP结果稳定且独立于块大小的选择。所提出的方法已成功用于对大型真实数据集（多光谱和高光谱图像）进行分区。

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