Feature extraction of hyperspectral image based on multilinear sparse principal components

The feature extraction of hyperspectral image helps to improve the application efficiency and accuracy of hyperspectral data.Considering the disadvantage that vector based feature extraction algorithm could not make full use of the cube spatial structure information of hyperspectral image,the multilinear sparse principal component analysis(MSPCA) algorithm was proposed to perform sparse dimensionality reduction in all tensor modes.Based on the hyperspectral images of typical villages in Jiaxing,China,and the currite mining area in Nevada,USA,three feature extraction methods i.e.,principal component analysis(PCA),spatial principal component analysis(SPCA) and multi-linear discriminant analysis(MPCA),were used to compare and analyze the classification accuracy of the proposed algorithm after feature extraction.The results showed that the classification accuracy of feature extraction obtained by MSPCA was better than that of the other three methods,and the overall classification accuracy of the proposed algorith