Semantic segmentation of hyperspectral images using convolutional neural networks and the attention mechanism

This paper investigates an effect of the attention mechanism on the accuracy of hyperspectral image segmentation by convolutional neural networks in agriculture. The study compares two modifications of neural network architectures: with and without the attention mechanism. The attention mechanism is implemented as two modules: position-based (PAM) and channel-based (CAM). The positional module (PAM) considers the global context using information about the spatial domain of the whole image. The channel module (CAM) in turn takes into account the information of all spectral components. L2Net and U-Net architectures are used for a comparative study. Modified versions with the addition of the attention mechanism are developed: L2AT-Net and ULAT-Net. The experimental results show that adding the attention mechanism to the U-Net and L2Net architectures increases the mean value of the F1 metric from 0.80 to 0.83 and from 0.74 to 0.78, respectively. The results show that the application of the attention mechanism can improve the quality of semantic segmentation of hyperspectral images.