Hyperspectral Image Classification of Brain-Inspired Spiking Neural Network Based on Attention Mechanism

Convolutional neural network (CNN) has a complex model structure in hyperspectral image (HSI) classification and the energy consumption during training and inference is high, so it cannot be applied in edge computing devices such as software-defined satellites and unmanned aerial vehicles. In order to solve the classification of HSI in an edge computing environment, inspired by the principle of neuro-dynamics and brain-inspired computing, we use integrate and fire neurons and shuffle squeeze and excitation (SE) module network to construct a spiking neural network (SNN-SSEM). This letter designs an approximate derivative backpropagation algorithm for discontinuous activation function and realizes the training of an SNN. Experiments were conducted on three HSI datasets and the average classification accuracy reached more than 99%. The energy consumption of our model is about 4.5 times that of CNN with the same architecture. This study is an exploration of the application of the scientific theory of brain-inspired computing in hyperspectral remote sensing technology, which can realize real-time classification of HSI in the mobile computing environment.