A Study on Enhancing the Visual Fidelity of Aviation Simulators Using WGAN-GP for Remote Sensing Image Color Correction  
[PERSON]  
1 Department of Defense Artificial Intelligence Applications, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea; [EMAIL_ADDRESS] (C.L. ); [EMAIL_ADDRESS] (H.K. ); [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (J.  
outside-the-window (OTW) imagery playing a critical role. Additionally, the need for new locations for overseas missions requires simulation training in a variety of environments, making terrain imagery accuracy and consistency critical. Terrain imagery is a key component of a simulator's visual representation of the Earth's surface features and is primarily obtained through aerial photography and satellite imagery. However, even when the same area is photographed, factors such as seasonal changes, lighting conditions, and cloud cover can lead to irregular terrain images. These irregular terrain images can be aesthetically unpleasant and, as a result, distracting to pilots. Currently, the Republic of Korea (ROK) Air Force requires human intervention in most processes to improve these images, leading to inconsistent results and increased work time. To address this issue, histogram matching [1; 2; 3; 4] has been widely used. This method adjusts the color histogram of the target image to align with the color distribution of the reference image. This method may not fully account for local variations or complex textures, leading to a loss of detail and potentially unnatural results when there are large color differences. Color transfer [5; 6; 7; 8] conveys a specific hue or mood by converting the colors of the target image to match the color statistics (e.g., mean, variance, etc.) of a reference image. This method also risks not preserving the structural features or textures of the image and can result in unnatural outcomes if the color difference is significant. To overcome these issues, Generative Adversarial Network (GAN)-based color correction techniques [9; 10; 11; 12; 13] have been proposed. GANs generate realistic data through adversarial learning between a Generator and a discriminator (later termed a Critic) and can convert irregular colors into consistent ones. However, there is a risk of mode collapse and overfitting for specific data during the learning process, and the loss function has an important impact on learning stability. In this study, the WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) [14] was used, which introduces a Gradient Penalty to overcome the limitations of traditional WGAN [15], improving learning stability and efficiency. By redefining the loss function using the Wasserstein distance concept and ensuring the Lipschitz continuity of the Critic, the mode collapse problem is mitigated, leading to more stable training and improved data quality. Mode collapse, if left unaddressed, can severely impact remote sensing image color correction by causing repetitive color patterns and limited diversity in generated images. This can lead to distorted color distributions that fail to accurately represent the wide variety of textures and conditions present in remote sensing data, compromising the realism needed for applications like aviation simulators [16]. By effectively addressing mode collapse, WGAN-GP ensures that the generated images maintain diverse and accurate color representations, which are crucial for achieving realistic and immersive simulations. While previous studies have utilized WGAN-GP on whole remote sensing images, there are few studies that have applied WGAN-GP to partial images. In this study, WGAN-GP was applied to partial images using the BigEarth-Net-S2 [17] dataset. This dataset consists of 549,488 Sentinel-2 image patches collected from 10 European countries and contains a wide variety of terrain, seasons, and weather conditions, making it a suitable dataset for training WGAN-GP-based models.