In this communication, we address the problem of unsupervised dimensionality reduction (DR) for hyperspectral images (HSIs), using nearest-neighbor density-based (NN-DB) approaches. Dimensionality reduction is an important tool in the HSI processing chain, aimed at reducing the high redundancy among the HSI spectral bands, while preserving the maximum amount of relevant information for further processing. Basically, the idea is to formalize DR as the process of partitioning the spectral bands into coherent band sets. Two DR schemes can be set up directly, one based on band selection, and the other one based on band averaging. Another scheme is proposed here, based on compact band averaging. Experiments are conducted with hyperspectral images composed of an AISA Eagle HSI issued from our acquisition platform, and the AVIRIS Salinas HSI. We evaluate the efficiency of the reduced HSIs for final classification results under the three schemes, and compare them to the classification results without reduction. We show that despite a high dimensionality reduction (< 8% of the bands left), the clustering results provided by NN-DB methods remain comparable to the ones obtained without DR, especially for GWENN in the band averaging case. We also compare the classification results obtained after applying other unsupervised or semi-supervised DR schemes, based either on band selection or band averaging, and show the superiority of the proposed DR scheme.