SAR Image Colorization Using Multidomain Cycle-Consistency Generative Adversarial Network

2020 ◽  
pp. 1-5
Author(s):  
Guang Ji ◽  
Zhaohui Wang ◽  
Lifan Zhou ◽  
Yu Xia ◽  
Shan Zhong ◽  
...  
Keyword(s):  
Sar Image ◽  
Generative Adversarial Network ◽  
Image Colorization ◽  
Adversarial Network

scholarly journals Double-Channel Guided Generative Adversarial Network for Image Colorization

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 21604-21617
Author(s):  
Kangning Du ◽  
Changtong Liu ◽  
Lin Cao ◽  
Yanan Guo ◽  
Fan Zhang ◽  
...  
Keyword(s):  
Generative Adversarial Network ◽  
Image Colorization ◽  
Adversarial Network ◽  
Double Channel

scholarly journals Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

2019 ◽  
Vol 11 (2) ◽  
pp. 135 ◽  
Author(s):  
Xiaoran Shi ◽  
Feng Zhou ◽  
Shuang Yang ◽  
Zijing Zhang ◽  
Tao Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Recognition ◽  
Super Resolution ◽  
Automatic Target Recognition ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Feature Characterization

Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method.

scholarly journals Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X

2018 ◽  
Vol 10 (10) ◽  
pp. 1597 ◽  
Author(s):  
Dongyang Ao ◽  
Corneliu Octavian Dumitru ◽  
Gottfried Schwarz ◽  
Mihai Datcu
Keyword(s):  
Sar Image ◽  
Sar Images ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Image Translation ◽  
Gram Matrices ◽  
Processing Resources ◽  
Ground Coverage

With more and more SAR applications, the demand for enhanced high-quality SAR images has increased considerably. However, high-quality SAR images entail high costs, due to the limitations of current SAR devices and their image processing resources. To improve the quality of SAR images and to reduce the costs of their generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images. This method is based on the analysis of hierarchical SAR information and the “dialectical” structure of GAN frameworks. As a demonstration, a typical example will be shown, where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated into a high-resolution SAR image (e.g., a TerraSAR-X image). A new algorithm is proposed based on a network framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial Network—Gradient Penalty) loss functions and Spatial Gram matrices under the rule of dialectics. Experimental results show that the SAR image translation works very well when we compare the results of our proposed method with the selected traditional methods.

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