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Airborne and satellite hyperspectral sensors can collect significant quantities of data, commonly terabits in size. Thus, there is a need for the development of computational cost effective algorithms that speed up processing, yet retain essential data quality and information. Endmember extraction is critical in the processing chain of hyperspectral data. Existing methods have primarily been demonstrated on small data sets so their usefulness with large data sets has not been fully explored. The objective of this paper is to adapt the Orthogonal Subspace Projection (OSP) and Spatial Spectral Endmember Extraction (SSEE) algorithms, such that they run efficiently on large data sets without the loss of global image endmember quality. This is demonstrated using a simulated and two real hyperspectral images, the last of which is a multi- flight-line survey. The two adapted methods (OSP SS and SSEE SS) make use of spatial sub-sampling via local endmember extraction to reduce the size of the original data set. This paper will demonstrate that this type of spatial sub-sampling retains the full volume of the data, and thus, the vertices of the simplex that represent the global image endmembers. The results also show that computational time is reduce by more than half using the adapted methods. For the large multi- flight-line survey SSEE SS was able to better retain endmembers of natural materials compared with OSP SS. These results illustrate the potential of spatial sub-sampling for other endmember extraction tools when dealing with multi-flight-line surveys.