# [PERSON] Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis
## 3 Results and Discussion
### Experiments
Therefore, the InfoGAN also seems improper to generate synthetic radar images, because it does not satisfy the first and second objectives. Figure 11 shows the graphical results of the LSGAN, and Figure 12 describes the architectures of the generator and discriminator of the LSGAN. The architectures are identical to the DCGAN, except the number of convolution filters: convolution layers, dense layers, and activation layers using the rectified linear unit (ReLU), LeakyReLU, and hyperbolic tangent function. It seems that the generated images form a circular pattern in all stages of the epochs. Additionally, it seems that the LSGAN learned features of individual original images in the training process because there is a slight difference between classes. Unfortunately, the generated images still include noise that is impossible to neglect outside the circle. Therefore, the LSGAN also seems improper to generate synthetic radar images, because it does not satisfy the first objective. Finally, Figure 13 describes the graphical results of the WGAN-GP, and Figure 14 represents the architectures of the generator and discriminator of the WGAN-GP. The architectures are identical to the DCGAN: convolution layers, dense layers, and activation layers using the rectified linear unit (ReLU), LeakyReLU, and hyperbolic tangent function. It seems that the generated images form a circular pattern in the entire stages of epochs. Additionally, it seems that the WGAN-GP learned features of individual original images in the training process, because there is a remarkable difference between classes, as shown in Figure 13g,h,i. The final results contain patterns inside the circle; the generated patterns are different between classes; and the generated images are dissimilar to the training images. Therefore, the WGAN-GP seems appropriate to generate synthetic radar images, because it satisfies all objectives. The SSIM index measures the perceptual difference between two similar images rather than determining which one is better. Therefore, we chose representative examples of each class of radar image in Figure 2 as references. Furthermore, we generated five images at 300 epochs from each GAN model to compare. Table 1 shows the derived SSIM index results. We separated the generated images at 300 epochs into three groups \(A\), \(B\), and \(C\) for convenience, which included the generated images of the GAN models of Class 0, Class 1, and Class 2, respectively: \(A=\) {Figure 3g, Figure 5g, Figure 7g, Figure 9g, Figure 11g, Figure 13g}; \(B=\) {Figure 3h, Figure 5h, Figure 7h, Figure 9h, Figure 11h, Figure 13h}; \(C=\) {Figure 3i, Figure 5i, Figure 7i, Figure 9i, Figure 11i, Figure 13i}. Every first row indicates experimental results between groups \(A\), \(B\), and \(C\) and original images are shown in the second column. The WGAN-GP has the best average SSIM index, which is almost 5 to 2000 times better than other GAN models: GAN (0.1241, 0.0075, 0.01500), CGAN (0.0003, 0.0020, 0.0111), DCGAN (0.0018, 0.0073, 0.0707), InfoGAN (0.0009, 0.0061, 0.0082), LSGAN (0.0018, 0.0060, 0.0057), and WGAN-GP (0.6086, 0.4611, 0.3308), where the first, second, and third elements indicate the average SSIM index for Class 0, Class 1, and Class 2, respectively. Therefore, we can conclude that Table 1 numerically underpins the graphical results: that the WGAN-GP generally has better results than the others. Figure 10: Modules of InfoGAN. Figure 9: Images generated by InfoGAN. Figure 11: Images generated by LSGAN. Figure 4:0 Modules of LSGAN. Figure 4:1 Modules of WGAN-GP  
Figure 4:2 Images generated by WGAN-GP.