Hyperspectral Image Classification Based on Extended Morphological Profile Features and Ghost Module

Hyperspectral image has a large amount of data and many feature channels. If the hyperspectral image classification model is too complex, it is easy to cause low efficiency. In order to reduce the amount of network calculation and model parameters, improve the operation efficiency, and make full use of the characteristics of hyperspectral data, this paper uses the Ghost module to reduce the complexity of the model, and combined with the extended morphological profile (EMP) features, proposes a hyperspectral image classification method based on extended morphological profile features and Ghost module (GhostEMP). The experimental results show that the proposed method can improve the efficiency of operation while ensuring the operation efficiency of the network model.