Hyperspectral images provide rich spectral information corresponding to visible and Near-infrared imaging (NIR) regions, facilitating accurate classication, object identication, and target detection. However, the high volume of data creates a computational challenge in processing. The band selection process identies specic informative and discriminative spectral bands from the data speed up the processing without impeding the performance. This paper presents an application-independent band selection framework that utilizes improved sparse deep subspace clustering and introduces an efcient multicriteria-based representative band selection (BS). The proposed sparse deep subspace clustering approach efciently identies the underlying non-linear subspace structure of the data and organizes the data accordingly. The work introduces a novel, robust sparsity measure to obtain a powerful self-representation and ameliorated performance compared to the prevalent subspace clustering methods. The work subsequently selects the representative bands from each cluster by combining structural information of the band images with the statistical similarity measure. We evaluate the BS performance on standard real images using information-theoretic criterion, classication, and unmixing performance. The comparative performance assessment demonstrates that our proposed method identies the informative bands and out.