Spectral unmixing is an important and challenging task in the field of hyperspectral data analysis. The existing methods of blind unmixing have certain limitations. In this paper

we present a new blind unmixing method

namely the ATGP-NMF algorithm

for hyperspectral imagery. The method is based on the improved target endmember acquired through integration of the Automatic Target Generation Process（ ATGP） algorithm and the Non-negative Matrix Factorization（ NMF）. The Harsanyi-FarrandChang（ HFC） algorithm was introduced firstly to determine the number of target endmembers. Then the ATGP algorithm and NonNegative Least Squares（ NNLS） were used to obtain the spectra and abundances of the target endmembers

which were then used as initial values for the NMF algorithm to obtain the refined endmembers. Finally

an improved cross correlogram spectral matching method was introduced to match the corresponding land cover type of each endmember. Three different sets of data

namely simulated data

laboratory-controlled spectral experimental data and remote sensing imagery

were used in this study to test the effectiveness and robustness of the proposed method

in comparison with the original NMF algorithm. Results from these experiments show that the ATGP-NMF algorithm can obtain endmembers with high accuracy and it is more robust and efficient than the original NMF algorithm in different situations

regardless of the existence of pure pixels

inter-class diversity

or correlation among the endmembers’ spectra. The ATGP-NMF algorithm thus has great potential of application in blind unmixing for hyperspectral imagery.