A Study of Hyperspectral Classification Combined with Minimum Noise Fraction and Variational Mode Decomposition

Supervised classification is one of the widespread applications in hyperspectral data analysis. Due to the large number of hyperspectral data bands and the redundancy of information between the bands, it brings great challenges to hyperspectral classification. The effect of hyperspectral data feature extraction determines the performance of classification accuracy. In order to improve the classification accuracy, this paper proposes a joint feature extraction method based on minimum noise fraction (MNF) and variational mode decomposition (VMD). The hyperspectral data is firstly minimum noise fraction transformed, and then the first few MNF sequences containing the main information of the hyperspectral are subjected to VMD. Then, identify and classify each mode component obtained by decomposing. Finally, through the support vector machine (SVM) classification and comparative analysis, the method has a good accuracy.