Event-driven spiking neural network based on membrane potential modulation for remote sensing image classification

Spiking neural network (SNN) based on sparse triggering and event-driven is a hardware-friendly model. SNN can provide an ultra-low power alternative for the deep neural network (DNN) to process remote sensing images. Brain information processing depends on the action potential of neurons. Therefore, the biological rationality of the artificial neural network (ANN) has been questioned. SNN is a more suitable model for brain information processing mechanisms. At present, the SNN obtained by ANN conversion has achieved the best performance in the current image processing tasks. However, the method based on ANN to SNN will have performance loss in the conversion process. Herein, we proposed a spiking neuron threshold-following reset (TF-reset) method and a membrane potential modulation method to reduce the loss of network conversion. We theoretically analyzed the proposed TF-reset and deduced the relationship between spike firing rate and neuron activation. In the experiment, we used an improved VGG-15 architecture combined with the method of transfer learning to apply the model to the classification task of remote sensing images. SNN-VGG-15 based on TF-reset and membrane potential modulation algorithm achieved a classification accuracy of 99.14%, 94.54%, and 95.00% on UCM, RSSCN7, and AID. Our algorithm can not only realize the lossless conversion of SNN but also outperforms the original network in classification performance on UCM and RSSCN7. In addition, our model also has advantages in energy consumption and noise robustness. The algorithm in this paper can provide a reference for the research remote sensing images procession using SNN.