Improved hybrid convolutional neural network combined with attention mechanism for hyperspectral image classification

Abstract

To address the problems of high dimensionality of hyperspectral images, small training samples and overfitting and too many parameters caused by model training, a hyperspectral image classification model (CBAM-HybridSN) with improved hybrid neural network combined with convolutional attention mechanism is proposed. The model firstly uses principal component analysis to remove the redundancy of spectral dimensional data, extracts the null spectral features by the hybrid neural network model, and introduces the convolutional attention module to rescale the extracted features and highlight the important features, thus improving the classification accuracy. In the experiments, the Pavia University dataset was divided into samples with 1:9, and the OA reached 99.32%, achieving accurate classification of hyperspectral images with small samples.