DeepSAR-Net: Deep convolutional neural networks for SAR target recognition

2017 ◽  
Author(s):  
Yang Li ◽  
Jiabao Wang ◽  
Yulong Xu ◽  
Hang Li ◽  
Zhuang Miao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Target Recognition ◽  
Deep Convolutional Neural Networks ◽  
Sar Target Recognition

scholarly journals SAR Target Recognition in Large Scene Images via Region-Based Convolutional Neural Networks

2018 ◽  
Vol 10 (5) ◽  
pp. 776 ◽  
Author(s):  
Zongyong Cui ◽  
Sihang Dang ◽  
Zongjie Cao ◽  
Sifei Wang ◽  
Nengyuan Liu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Target Recognition ◽  
Sar Target Recognition

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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