In the past decade, there has been a growing need for machine learning and computer vision components (segmentation, classification) in the hyperspectral imaging domain. Due to the complexity and size of hyperspectral imagery and the enormous number of wavelength channels, the need for combining compact representations with image segmentation and superpixel estimation has emerged in this area. Here, we present an approach to superpixel estimation in hyperspectral images by adapting the well known UCM approach to hyperspectral volumes. This approach benefits from the channel information at each pixel of the hyperspectral image while obtaining a compact representation of the hyperspectral volume using principal component analysis. Our experimental evaluation demonstrates that the additional information of spectral channels will substantially improve superpixel estimation from a single "monochromatic" channel. Furthermore, superpixel estimation performed on the compact hyperspectral representation outperforms the same when executed on the entire volume.