| Title |
Simulation and Colorization between Gray-scale Images and Satellite SAR Images Using GAN |
| Authors |
조수민(Jo, Su Min) ; 허준혁(Heo, Jun Hyuk) ; 어양담(Eo, Yang Dam) |
| DOI |
https://doi.org/10.12652/Ksce.2024.44.1.0125 |
| Keywords |
영상복원; 고해상도 SAR 영상; 영상변환; CycleGAN; Pix2pix Image restoration; High-resolution SAR image; Image translation; CycleGAN; Pix2pix |
| Abstract |
Optical satellite images are being used for national security and collection of information, and their utilization is increasing. However, it acquires low-quality images that are not suitable for the user's requirement due to weather conditions and time constraints. In this paper, a deep learning-based conversion of image and colorization model referring to high-resolution SAR images was created to simulate the occluded area with clouds of optical satellite images. The model was experimented according to the type of algorithm applied and input data, and each simulated images was compared and analyzed. In particular, the amount of pixel value information between the input black-and-white image and the SAR image was similarly constructed to overcome the problem caused by the relatively lack of color information. As a result of the experiment, the histogram distribution of the simulated image learned with the Gray-scale image and the high-resolution SAR image was relatively similar to the original image. In addition, the RMSE value was about 6.9827 and the PSNR value was about 31.3960 calculated for quantitative analysis. |