Novel hyperbolic clustering-based band hierarchy (HCBH) for effective unsupervised band selection of hyperspectral images

For dimensionality reduction of HSI, many clustering-based unsupervised band selection (UBS) methods have been proposed due to their superiority of reducing the high redundancy between selected bands. However, most of these methods fail to reflect the data structure of HSI, leading to inconsistent results of band selection. To tackle this particular issue, we have proposed a novel hyperbolic clustering-based band hierarchy (HCBH) to fully represent the underlying spectral structure and obtain a more consistent band selection. With the proposed adaptive hyperbolic clustering, the performance can be effectively improved with the aid of geometrical information. By introducing a cluster-centre based ranking metric, the desired band subset can be naturally obtained during the clustering process. Experimental results on three popularly used datasets have validated the superior performance of the proposed approach, which outperforms a few state-of-the-art (SOTA) UBS approaches.