How to cite:
Chen, J.; Qiu, X.; Han, C.; Wu, Y. Unsupervised Learning Method for SAR Image Classification Based on Spiking Neural Network. Preprints2021, 2021020083. https://doi.org/10.20944/preprints202102.0083.v1
Chen, J.; Qiu, X.; Han, C.; Wu, Y. Unsupervised Learning Method for SAR Image Classification Based on Spiking Neural Network. Preprints 2021, 2021020083. https://doi.org/10.20944/preprints202102.0083.v1
Chen, J.; Qiu, X.; Han, C.; Wu, Y. Unsupervised Learning Method for SAR Image Classification Based on Spiking Neural Network. Preprints2021, 2021020083. https://doi.org/10.20944/preprints202102.0083.v1
APA Style
Chen, J., Qiu, X., Han, C., & Wu, Y. (2021). Unsupervised Learning Method for SAR Image Classification Based on Spiking Neural Network. Preprints. https://doi.org/10.20944/preprints202102.0083.v1
Chicago/Turabian Style
Chen, J., Chuanzhao Han and Yirong Wu. 2021 "Unsupervised Learning Method for SAR Image Classification Based on Spiking Neural Network" Preprints. https://doi.org/10.20944/preprints202102.0083.v1
Abstract
Recent neuroscience research results show that the nerve information in the brain is not only encoded by the spatial information. Spiking neural network based on pulse frequency coding plays a very important role in dealing with the problem of brain signal, especially complicated space-time information. In this paper, an unsupervised learning algorithm for bilayer feedforward spiking neural networks based on spike-timing dependent plasticity (STDP) competitiveness is proposed and applied to SAR image classification on MSTAR for the first time. The SNN learns autonomously from the input value without any labeled signal and the overall classification accuracy of SAR targets reached 80.8%. The experimental results show that the algorithm adopts the synaptic neurons and network structure with stronger biological rationality, and has the ability to classify targets on SAR image. Meanwhile, the feature map extraction ability of neurons is visualized by the generative property of SNN, which is a beneficial attempt to apply the brain-like neural network into SAR image interpretation.
Keywords
SAR image classification; Spiking Neural Network(SNN); unsupervised learning
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
