Authors: Sneha Kanawade, Dr. Suvarna Patil, Siddhi Kadu, Aniruddha Ojha, Aryan Sahu, Indra Pratap Singh Rajawat
Abstract: Synthetic Aperture Radar is vital remote sensing technology, offering all-weather, day-and- night imaging ca-pabilities. However, its inherent grayscale nature, along with speckle noise, presents significant challenges for interpretation by non-specialists. This review addresses recent advancements in applying deep learning to SAR colorization, a technique aimed at enhancing visual interpretability of these images while preserving unique radiometric properties. The primary motivation is to bridge the gap between complex radar data, intuitive visual analysis, thereby broadening its application in fields like disaster management, environmental monitoring. Major themes covered include critical distinction between grayscale colorization, SAR-tooptical translation, evolution of methodologies from tradi-tional regression to advanced deep learning models, lack of standardized evaluation protocols that has hindered progress. Existing technologies often involve convolutional neural networks, Generative Adversarial Networks (GANs). This review high-lights a proposed methodology centered on conditional GAN within a complete benchmarking protocol utilizing synthetically generated ground truth via intensity-high saturation (IHS) fusion. Key features of this approach include an end-to-end supervised learning framework, use of domain-specific evaluation metrics (Q4, NRMSE, SAM). This advancement holds significant impli-cations for real-time disaster response, contributes to Sustainable Development Goals (SDGs) such as ”Sustainable Cities and Com-munities”, ”Climate Action” by making critical environmental data more accessible, actionable.
