With the increase in the number and availability of imaging sensors, data size or dimensionality reduction, compression, archiving and retrieval algorithms are gaining importance. Whereas in the past, for hyperspectral images, the size reduction has been mostly concerned with the spectral domain, the increase in the number and spatial sizes of the hyperspectral images has raised the question whether a spatial reduction is also feasible. However, this reduction should be conducted in a content-aware fashion and should preserve the important and relevant information in the scene whether for archiving and retrieval purposes, or for following image processing tasks. In this work, an anomaly-preserving content-aware size reduction approach is proposed for hyperspectral images. The approaches utilizes seam carving with a spatial homogeneity energy function to preserve anomalies while performing reduction. Synthetic and real datasets are used to validate the proposed methodology.