Band selection and feature extraction are the two paradigms of hyperspectral dimensionality reduction. While feature extraction methods have many desirable properties, band selection methods keep the actual reflectances intact, which results in better interpretation of the behavior of the materials. Keeping this in mind, several feature extraction-based band selection methods have been proposed in the past, which can bridge the gap between these two approaches and combine their salient advantages. In this article, a feature extraction-based, clustering-ranking type band selection method is proposed in which the band selection is performed in two stages. In the clustering stage, a new and improved representation of bands using their component loadings for the top principal components is introduced, which is employed to cluster similar bands into groups using sparse subspace clustering. In the ranking stage, a new metric called combined divergence-correlation index is introduced to elect the most discriminative as well as least correlated bands as cluster representatives. Experimental results indicate that the proposed method can proficiently select a set of distinct and discriminative bands, which can help in effective hyperspectral classification. Additionally, it was also shown that the selected bands show rich information content, low interband correlation and high-class separabilities.