Data dimensionality reduction is crucial for hyperspectral imaging, whether to address the Hughes phenomenon or decrease the computational cost and memory requirements. Whereas up until recently, the majority of dimensionality reduction approaches for hyperspectral images have operated in the spectral domain, the spatial sizes of the hyperspectral images have also been increasing, and as a result, reduction or compression of hyperspectral images in the spatial domain is also gaining importance. However, spatial size reduction is often problematic as it heavily depends on the contents of the image in question. In this paper, seam carving, a content-aware image size reduction approach, is adapted to hyperspectral images for spatial size reduction. The approach is evaluated using multiple energy functions, and the potential and advantages of content-aware size reduction in the spatial domain for hyperspectral images is presented and validated through the example application of unmixing.