Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images

Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images

The computational cost of many deep convolutional neural networks (CNNs) for the automatic target recognition proposed in the area of synthetic aperture radar (SAR) imagery recently is huge, and the limited SAR images are always insufficient for training the deep CNNs. To improve the computational e...

Full description

Bibliographic Details
Main Authors: Zelong Wang, Xianghui Xu
Format: Article
Language:English
Published: Wiley 2019-10-01
Series:The Journal of Engineering
Subjects:
radar target recognition
learning (artificial intelligence)
synthetic aperture radar
image recognition
feature extraction
convolution
neural nets
image classification
radar imaging
efficient deep convolutional neural networks
sar images
computational cost
automatic target recognition
synthetic aperture radar imagery
deep cnns
computational efficiency
efficient convolutional network architecture
convolutional layer
internal covariate shift
fully layer
authors
sar target classification
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0567

Internet

https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0567

Similar Items