Deep Convolutional Highway Unit Network for SAR Target Classification With Limited Labeled Training Data

The deep convolutional neural network (CNN) has been widely used for target classification, because it can learn highly useful representations from data. However, it is difficult to apply a CNN for synthetic aperture radar (SAR) target classification directly, for it often requires a large volume of...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 14; H. 7; S. 1091 - 1095
Hauptverfasser:	Lin, Zhao, Ji, Kefeng, Kang, Miao, Leng, Xiangguang, Zou, Huanxin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Architecture Classification Convolutional neural network (CNN) Data deep learning Feature extraction feature representation highway network Model accuracy Neural networks Radar Representations Road transportation SAR (radar) Stacking Synthetic aperture radar syntheticaperture radar (SAR) Target acquisition Target recognition Training Training data Transforms Transportation networks
ISSN:	1545-598X, 1558-0571
Online-Zugang:	Volltext
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