Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Unsupervised Learning Method for SAR Image Classification Based on Spiking Neural Network

Version 1 : Received: 30 January 2021 / Approved: 2 February 2021 / Online: 2 February 2021 (10:35:38 CET)
(This article belongs to the Research Topic EUSAR 2020—Preprints)

How to cite: Chen, J.; Qiu, X.; Han, C.; Wu, Y. Unsupervised Learning Method for SAR Image Classification Based on Spiking Neural Network. Preprints 2021, 2021020083 (doi: 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 (doi: 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.

Subject Areas

SAR image classification; Spiking Neural Network(SNN); unsupervised learning

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