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Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection

Hyperspectral band selection is a commonly used technique to alleviate the curse of dimensionality. Recently, clustering-based methods have attracted much attention for their effectiveness in selecting informative and representative bands. However, the single clustering algorithm is used in most of the clustering-based methods, and the neglect of the correlation among adjacent bands in their clustering procedure is prone to resulting in the degradation of the representativeness of the selected band set. This may, consequently, adversely impact hyperspectral classification performance. To tackle such issues, in this paper, we propose a correlation-guided ensemble clustering approach for hyperspectral band selection. By exploiting ensemble clustering, more effective clustering results are expected based on multiple band partitions given by base clustering with different parameters. In addition, given that adjacent bands are most probably located in the same cluster, a novel consensus function is designed to construct the final clustering partition by performing an agglomerative clustering. Thus, the performance of our addressed task (band selection) is further improved. The experimental results on three real-world datasets demonstrate that the performance of our proposed method is superior compared with those of state-of-the-art methods.