Deep Neural Network Initialization Methods for Micro-Doppler Classification With Low Training Sample Support

Deep neural networks (DNNs) require large-scale labeled data sets to prevent overfitting while having good generalization. In radar applications, however, acquiring a measured data set of the order of thousands is challenging due to constraints on manpower, cost, and other resources. In this letter,...

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Vydané v:	IEEE geoscience and remote sensing letters Ročník 14; číslo 12; s. 2462 - 2466
Hlavní autori:	Seyfioglu, Mehmet Saygin, Gurbuz, Sevgi Zubeyde
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.12.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Classification Convolutional autoencoders (CAEs) convolutional neural networks (CNN) Data Data acquisition Data transfer (computers) Datasets Decoding Doppler sonar Feature extraction gait classification Learning Manpower micro-Doppler Model accuracy Neural networks Radar Radar imaging Radar signatures Spectrogram Training Transfer learning vGGNet
ISSN:	1545-598X, 1558-0571
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