Due to the large amount of image data, most algorithms for endmember extraction cost huge time, which limits the wide application of them. A fast endmember extraction algorithm is proposed by using Spectrum Gradient Features as the searching rule. The core idea is composed of two parts, namely, rapid screening of candidate endmembers based on Spectral Gradient Features and endmember identification based on spectrum unmixing residual. Being able to quickly screen out a small amount of pixels from the image as candidate endmembers, the algorithm has excellent computational performance. This algorithm can also avoid non-endmember spectrum participating in endmember identification and can obtain a result of higher accuracy. The experimental result shows that this new algorithm can greatly improve the endmember extraction speed and recognize endmembers more accurately compared with IEA and ECHO. What's more, existing algorithms for endmember extraction can be applied better based on the principle of this algorithm, and the extraction speed can be improved remarkably.
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