Research Article
Analysis of the Influence of Convolutional Layer in Deep Convolutional Neural Network on SAR Target Recognition
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@INPROCEEDINGS{10.1007/978-3-030-69066-3_35, author={Wei Qu and Gang Yao and Weigang Zhu}, title={Analysis of the Influence of Convolutional Layer in Deep Convolutional Neural Network on SAR Target Recognition}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={Deep convolutional neural network SAR Target recognition}, doi={10.1007/978-3-030-69066-3_35} }- Wei Qu
Gang Yao
Weigang Zhu
Year: 2021
Analysis of the Influence of Convolutional Layer in Deep Convolutional Neural Network on SAR Target Recognition
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_35
Abstract
As a frontier hot spot in the current image processing field, deep learning has unparalleled superiority in feature extraction. Deep learning uses deep network structure to perform layer-by-layer nonlinear transformation, which can achieve the approximation of complex functions. From low-level to high-level, the representation of features becomes more and more abstract, and the more essential the original data is described. Aiming at the problem of SAR image target detection, this paper studies the influence of the number of convolution kernels, the size of the convolution kernel and the number of convolution layers in the deep convolutional neural network on SAR target recognition.
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