Hyperspectral Image Classification Based on Convolution Neural Network with Attention Mechanism

In recent years, convolutional neural network (CNN), as a representative of the deep learning method, has gradually become a research hotspot in the field of hyperspectral image (HSI) classification because it does not require complex data preprocessing and feature design. In this paper, a deep CNN model with an attention mechanism is proposed based on an existing neural network model combined with the HSI data characteristics. The model used a residual structure to construct a deep CNN to extract spatial-spectral features and introduced a channel attention mechanism to recalibrate the extracted features. According to different importance levels of features, the attention mechanism assigned different weights to features on different channels, highlighted important features, and controlled unimportant features. Experiments were conducted at Indian Pines and Pavia University to validate the proposed technique. When the spatial size of the dataset was 19 x 19, the Indian Pines and Pavia University datasets were divided into 3 : 1 : 6 and 1 : 1 : 8, respectively. Additionally, these datasets have the best classification accuracy. The average overall accuracy, average accuracy, and average Kappa coefficient obtained are 99. 55%, 99. 31%, and 99. 45%, respectively. The experimental results show that deep CNN with residual structure can extract high spatial-spectral features of the HST. Additionally, the attention mechanism recalibrates the features to strengthen the important features, thereby effectively enhancing the HSI's classification accuracy.