Analysis of hyperspectral imagery requires the extraction of certain basis spectra called endmembers, which are assumed to be the pure signatures in the image data. N-FINDR algorithm developed by Winter [1] is one of the most widely used technique for endmember extraction. This algorithm is based on the fact that in L spectral dimensions, the L-dimensional volume contained by a simplex formed from the purest pixels is larger than any other volume formed from other combination of pixels. Recently proposed algorithm based on virtual dimensionality (VD) determines the number of endmembers present in the dataset, where an endmember initialization algorithm (EIA) is used to select an appropriate set of pixels for initializing the N-FINDR process. In this paper, we proposed a fast algorithm to implement the N-FINDR technique which has much better computational efficiency than the existing techniques. In the proposed technique, we used the VD to find the number of endmembers N. Then we reduced the dimensionality of the hyperspectral dataset to N−1 by using the principal component transformation (PCT) and divided all the pixels into N number of classes by using the spectral angle map (SAM). We extracted N number of the most pure pixels from each group by using the classical N-FINDR algorithm but with exhaustive search. Thus we get N 2 pixels that are most likely to be the actual endmembers. The classical N-FINDR algorithm is then again applied on these selected pixels to find the final N endmember. Grouping the pixels into several classes makes the computation very fast. Since we extracted N number of pixels from each group by exhaustive search, there is no possibility of loosing any endmember due to classification.

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