Interactive SAR Image Colorization Using Conditional GANs, ResNet and Generative Adversarial Refinement with Real-Time Insights

SAR images are traditionally monochromatic, rich in structural information but lack the intuitive insights. This manuscript enhances the analysis of Synthetic Aperture Radar (SAR) images by developing an advanced colorization system that utilizes deep learning techniques. The proposed system utilizes Conditional Generative Adversarial Networks (cGANs) to produce realistic colorized representations of SAR images, incorporates ResNet for effective feature extraction, and employs Generative Adversarial Refinement (GAR) to enable user-driven improvements. Furthermore, the integration of LLaMA facilitates interactive insights, enabling users to interrogate specific features within the images and refine the colorization process based on received feedback. This interactive, user-friendly approach improves the usability of SAR data for applications such as environmental monitoring, urban planning, logistics and disaster management, thus offering a scalable and efficient solution for SAR image analysis.