BR 3 L: Rethinking Bilateral Reweighted Reconstruction Representation Learning for Unsupervised Hyperspectral Band Selection

Unsupervised band selection aims to identify the most informative spectral bands from hyperspectral images, thereby reducing spectral redundancy while preserving essential spatial–spectral structures. However, existing methods often ignore global or local structural information and noise, leading to suboptimal band subsets. In this paper, we propose a bilateral reweighted reconstruction representation model that jointly leverages spatial and spectral priors for unsupervised band selection. To si