Abstract
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.
Highlights
The interest in hyperspectral image data has been constantly increasing during the last years
We have addressed the two main problems of Affinity propagation (AP), i.e., (1) the difficulty in handling datasets with a high number of points, and (2) the difficulty in linking the preference parameter to the final number of classes (NCs) provided by AP
We have proposed using an automatic search of the optimal preference parameter to estimate the correct NCs present in an image
Summary
The interest in hyperspectral image data has been constantly increasing during the last years. In Ref. 9, a semi-supervised method introducing a feature metric into AP as the criterion for spectral band selection was proposed In all these publications, AP is shown to provide the best results in band selection on a set of various hyperspectral images (from AVIRIS, HYDICE, and HYPERION sensors) with respect to several other approaches including maximum-variance principal component analysis, information divergence, and mutual information. In Ref. 11, Yang et al proposed a semi-supervised AP clustering approach based on Incremental Decremental learning It is applied on three different types of multispectral images for land cover classification and successfully compared with semi-supervised clustering algorithms: constrained K-means, incremental AP, and maximum likelihood and Mahalanobis distance. The last section concludes this work and gives some perspectives
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