ABSTRACT Band selection in hyperspectral imaging is a burgeoning research area whose aim is to select a small number of bands in order to reduce data redundancy and noise bands. The existing ranking‐based methods face two challenges: (1) The density calculation using nearest neighbours only considers distances between bands, ignoring shared neighbours. Thus, it fails to reflect the local distribution of bands. (2) The high dimensionality of the bands limits the effectiveness of the Euclidean distance‐based metric in accurately capturing their similarity. To address the issues, we've proposed an innovative approach for selecting bands, grounded in a mass‐based metric and shared nearest neighbours called MBSNN. Initially, we leverage a mass‐based metric computation technique to supplant the conventional distance metric between disparate bands. This substitution mitigates the distortions that high‐dimensional data can inflict on distance calculations. Subsequently, the natural nearest neighbour method is combined to calculate the local density of the band, reflecting its local distribution characteristics. Finally, an information entropy and peak synergy band selection technique is constructed. To substantiate the merits of our proposed approach, we executed experiments utilising support vector machines across four benchmark datasets. The results of these experiments affirm the effectiveness of our band selection approach.