A Multi-Stage Deep Learning Framework for SAR Image Colorization with Physics-Aware Fusion and Distribution Alignment

This paper introduces a comprehensive multi-stage deep learning framework designed to address the significant interpretability challenges inherent in Synthetic Aperture Radar (SAR) imagery through physically-consistent colorization. The proposed architecture represents a paradigm shift from conventional SAR-to-optical translation approaches by systematically integrating data-driven learning with physical scattering principles through five specialized processing stages. The framework begins with robust feature extraction using a modified ResNet18 architecture optimized for SAR-specific characteristics including speckle noise resilience and fine texture preservation. Subsequent stages employ attention-enhanced U-Net for semantic color space reconstruction, terrain-conditioned Pix2Pix GAN for adversarial refinement, Non-Subsampled Shearlet Transform (NSST) for physicsaware multi-scale fusion incorporating polarization data, and Schrödinger Bridge networks for theoretical distribution alignment between SAR and optical domains. The methodology uniquely incorporates terrain semantics from ESA WorldCover and physical scattering information from ALOS PALSAR to ensure geographically and physically consistent color assignments. This conceptual paper establishes the theoretical foundations, architectural innovations, and methodological advantages of the proposed framework, setting the stage for experimental validation in subsequent research.