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.
The affinity propagation (AP)1 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).
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