Feature extraction and classification of hyperspectral images based on improved two-dimensional compact variational mode decomposition

In order to solve the problem of noise interference in hyperspectral image (HSI) feature extraction, an improved two-dimensional compact variational mode decomposition (2D-C-VMD) method is proposed to extract the spatial dimension feature information of HSI. By smoothing the low-frequency mode components obtained from the first decomposition of 2D-C-VMD, the method is called L-2D-C-VMD, which can further eliminate the adverse effects of spatial domain noise on the classification of different ground objects to a certain extent. Through simulation test analysis on the public datasets Indian Pine and Pavia University. When the Indian Pine training sample takes 4 %, the OA value of the proposed classification method can be increased by about 2 %. When the Pavia University training sample is taken at 0.5 %, the OA value of the proposed classification method can be increased by nearly 3 %, and the proposed method has better competitiveness with fewer training samples.