EMC2A-Net: An Efficient Multibranch Crosschannel Attention Network for SAR Target Classification

In recent years, convolutional neural networks (CNNs) have demonstrated significant potential for synthetic aperture radar (SAR) target recognition. SAR images possess a strong sense of granularity and contain texture features of varying scales, including speckle noise, dominant scatterers and targe...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2023-06, p.1-1
Hauptverfasser:	Yu, Xiang, Qian, Ying, Geng, Zhe, Huang, Xiaohua, Wang, Qinglu, Zhu, Daiyin
Format:	Artikel
Sprache:	eng
Schlagworte:	Channel attention Convolution Convolutional neural networks deep convolutional neural network (DCNN) Electromagnetic compatibility Feature extraction isotopic architecture Kernel multiscale feature fusion Radio frequency Synthetic aperture radar synthetic aperture radar (SAR) target classification
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