Deep Learning Driven: Approach for SAR Image Colorization

Abstract - To enhance the colorization of Synthetic Aperture Radar (SAR) images, which often lack fine details, a new approach is proposed. Traditional methods, such as CycleGAN, frequently result in blurry images with inaccurate colors. The proposed method utilizes a deep learning model to improve color accuracy and maintain fine details during colorization. It ensurs that the most important features are accurately represented. Furthermore, accuracy is assessed and refined after colorization to ensure optimal results. This innovative approach overcome the limitations of previous methods, making SAR images more interpretable and useful for applications like geological studies and environmental monitoring. Key Words: SAR, Image Colorization, Deep Learning, Detail Preservation, Image Processing.