Two-dimensional compact variational mode decomposition for effective feature extraction and data classification in hyperspectral imaging

Abstract

.Two-dimensional compact variational mode decomposition (2-D-C-VMD) is a new data mining and signal analysis technology. It is suitable for feature extraction and classification of hyperspectral images (HSIs). However, given an appropriate prior parameter K value is the primary condition for us to achieve 2-D-C-VMD for feature extraction. Different prior-parameter K values will produce results beyond our expectations in different application scenarios. Consequently, combining HSIs as an application scenario and aiming for feature extraction and classification recognition effect, we determine the optimal prior parameter K* of 2-D-C-VMD and construct a set of optimal high-pass, low-pass, and band-pass filters using the optimal threshold K* to improve the classification accuracy. In the experiment, three open HSIs data sets with different characteristics were used to verify the feasibility and advantages of the scheme, and the test analysis was carried out under different training sets. The research results show that the scheme has better competitiveness in the comprehensive classification accuracy.