Remote Sensing Imagery Scene Classification Based on Spiking Neural Network
Saifei Wu, Jie Li, Lin Qi, Ziming Liu, Xinbo Gao
Abstract
In order to overcome the large computational cost of deep neural networks (DNNs), spiking neural networks (SNNs) have been proposed, which are more biologically reasonable. It has the potential to achieve energy efficiency while maintaining performance comparable to DNNs. Although SNNs have achieved good results on the MNIST and CIFAR10 data sets, its potential in remote sensing has not been studied and explored. This paper adopts the idea of converting a trained DNN into an SNN, and proposes a multi-bit-based SNN, and introduces channel normalization (channel-norm) to replace the previous layer normalization to achieve remote sensing imagery scene classification tasks. By achieving multi-bit spiking and channel-norm in the way of DNN conversion to SNN, the SNN proposed in this paper achieves lossless conversion on the UC Merced data set and WHU-RS data set.