# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 4 Experiments
### Performance Analysis and Comparisons  
We analyze and discuss the performance variation of the model based on the number of Residual Block repetitions, denoted as \(N\). Table 3 presents the performance results from the experiments. Performance is assessed using the metrics PSNR, SSIM, and LPIPS [28], an upward arrow indicates that higher values represent better performance, while a downward arrow signifies that lower values correspond to better performance. PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) are objective image quality measures, with higher values indicating more remarkable similarity to the original image. LPIPS (Learned Perceptual Image Patch Similarity) measures the similarity between images by comparing feature maps extracted using a VGG network, where lower values indicate higher similarity. When \(N\) is equal to 15, the PSNR value is 30.735, which is indicative of optimal performance. An examination of the PSNR values from \(N=9\) to \(N=17\) reveals that an increase in the number of Residual Block repetitions does not necessarily result in enhanced performance. It is, therefore, imperative to select an appropriate value for \(N\). SSIM is a metric that evaluates the structural similarity of images, and similarly, higher values indicate better performance. The SSIM value is highest at 0.806 when \(N\) is 15, indicating that the structural similarity of the image is best preserved with this number of Residual Block repetitions. LPIPS measures the learned perceptual image patch similarity; lower values indicate better performance. The LPIPS value is lowest at 0.304 when \(N\) is 15, indicating the best perceptual quality of the image. A comprehensive analysis of the experimental results shows that the model achieves optimal performance in terms of PSNR, SSIM, and LPIPS when the number of Residual Block repetitions, \(N\), is 15. It demonstrates that increasing the number of Residual Block repetitions enhances the model's performance up to a certain point, but further increases can lead to diminishing returns or even performance degradation. Therefore, selecting an optimal number of Residual Block repetitions is crucial for maximizing the model's performance. This study found that \(N=15\) provides the optimal performance, indicating that this number of repetitions best preserves the information from the aerial images while generating map images from aerial images. Table 4 presents the results of evaluating and comparing the performance of various models for generating maps from aerial images using GAN-based techniques. In the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**N** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) & **LPIPS**\(\downarrow\) \\ \hline
9 & 30.399 & 0.800 & 0.313 \\
11 & 30.280 & 0.797 & 0.318 \\
13 & 30.554 & 0.803 & 0.312 \\
15 & **30.735** & **0.806** & **0.304** \\
17 & 30.580 & 0.801 & 0.309 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results based on the number of residual block repetition (\(N\)). experiments, the performance of the proposed model is analyzed in comparison with the existing Pix2 Pix [6], CycleGAN [7], and SMAPGAN [16]. The proposed model is evaluated under different configurations, including using Residual Blocks and applying \(\lambda_{\text{Edge}}\). The experimental results demonstrate that the proposed model, which integrates Residual Blocks and \(\lambda_{\text{Edge}}\), achieves the highest values in PSNR and SSIM. This suggests that the quality of the generated map images is improved compared to other models. Specifically, the proposed method records a PSNR of 30.735, an SSIM of 0.806, and an LPIPS of 0.304, indicating superior performance across these evaluation metrics. Analysis of the results shows a significant improvement in model performance with the application of Residual Blocks, highlighting their role in preserving image features. Although performance improvement is also observed using \(\lambda_{\text{Edge}}\), it is not as pronounced.