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Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently spectrally unmix the data, three key steps must be accomplished: Estimate the number of endmembers (EMs), identify the EMs, and then unmix the data. Several different statistical and geometrical approaches have been developed for all steps of the unmixing process. However, many of these methods rely on using the full image to estimate the number and extract the EMs from the background data. In this paper, spectral unmixing is accomplished using a spatially adaptive approach. Linear unmixing is performed per pixel with EMs identified at the local level, but global abundance maps are created by clustering the locally determined EMs into common groups. Results show that the unmixing residual error of each pixel's spectrum from real data, estimated from the spatially adaptive methodology, is reduced when compared to a global scale EM estimation and linear unmixing methodology. The component algorithms of the new spatially adaptive approach, which complete the three key unmixing steps, can be interchanged while maintaining spatial information, making this new methodology modular. A final advantage of the spatially adaptive spectral unmixing methodology is the user-defined spatial scale size.