Hyperspectral detection is an important method for qualitatively identifying substances, and spectral unmixing is the key to hyperspectral analysis and identification. The blind source unmixing separation method based on weighted non-negative matrix factorization (NMF) hyperspectral reflection curves are established for the spectral decomposition and identification of minerals after mixing by using the NMF blind source unmixing method to address the problem of inaccurate analysis of compound or mineral mixed spectral in the paper. The algorithm assumes that the spectral mixing model is a linear combination of scaled component spectral signals, uses the minimum Euclidean distance and reweighted sparsity constraints to establish the combination conditions to promote the sparsity of the unmixing matrix, and carries out the iterative calculation of the unmixing NMF constraint with the initial weight of the spectral angular cosine of the mixing spectra and component spectral basis vectors to finally decompose the source spectral basis vectors and the abundance matrix of the mineral mixing spectral. Three mixtures of chemically pure CuO and Cu2O, Cu (OH)(2) and Cue (OH)(2)CO3, malachite and azurite hyperspectral profiles were selected for spectral unmixing and identification experiments. After the measured mixture spectral curves were equalized and whitened, the blind source unmixing calculation based on the weighted NMF hyperspectral reflection curve was carried out, and the unmixing performance index PI, the root mean square error of the spectral and the angular distance of the spectral were selected as the evaluation indexes of the unmixing effect. The experimental results show that the blind source unmixing effect of the NMF unmixing method is very obvious, the base source spectral features can be accurately separated based on unknown mixed spectral a priori conditions. The sample separation accuracy is less than 0. 15. The curves of the de-mixed and the source spectral have the same overall trend, maintaining similar absorption positions and absorption peaks of the source spectral, with minor shifts and obvious differences in reflectance values of the corresponding absorption positions. After adding 5% 15% Gaussian noise to the mixed spectral data, a weighted NMF-based unmixing process was performed, and it was found that the unmixing separation accuracy decreased slightly as the noise increased. However, the overall angular distance of the spectral and the root mean square error did not change significantly after unmixing, indicating that the NMF unmixing algorithm has good noise immunity and applies to spectra unmixing of measured non-pure material, which provides a basic theory for the identification and separation of mineral components after mixing.
