In this paper, we present a set of numerical tools, namely principal component analysis, clustering methods, and a covariance propagation model, that when appropriately assembled, form what we refer to as the mean-class propagation (MCP) method. The MCP method generates clusters of similar class materials in hyperspectral imaging (HSI) scenes while preserving scene spectral clutter information for radiometric transport modeling. We will demonstrate how various implementations of the MCP method can be employed to generate unique HSI products with varying levels of statistical realism across regions in the scene. Such implementations of the MCP method, compared with traditional pixel-based methods, may allow for faster generation of HSI scene data, better insight on how environmental conditions alter the statistical properties of measured scene clutter, and lays a foundation for the formulation of more robust spectral matched filter operations. To quantify the differences between the MCP method and a pixel-based method, we present a comparison computational processing time for each method.