Spectral Information Unmixing of Mixed Pigment Based on Clustering Optimization FastICA Algorithm

A clustering-optimized fast independent component analysis (FastICA) de-mixing algorithm is proposed. It solves the problem of unstable de-mixing information caused by the sensitivity to the initial value of the de-mixing matrix for FastICA algorithm during the spectral information de-mixing process. Fuzzy C-means clustering algorithm is used to reduce the spectral characteristics of single pigment spectral information, the most representative clustering result is selected as the initial value of the de-mixing matrix, and the clustering-optimized de-mixing matrix is calculated by FastICA Newton iteration formula to avoid the effect of randomly selecting initial values on de-mixing the spectral information of mixed pigments. The experimental results show that, compared with other algorithms, the average error value of the unmixed results of this algorithm is reduced by 0.57, the average fitness coefficient is 99.67%, and the spectral angle matching distance is reduced by 0.53. The proposed method can increase the stability of the FastICA de-mixing results, and improve the de-mixing precision of the mixed pigment spectral information.