m2rDM: A Map-Conditioned Diffusion Model for Maritime X-Band Radar Synthesis and Dynamic Object Suppression

Abstract

Autonomous vessel operation relies on X-band radar for navigation because it offers greater robustness than optical sensors under adverse weather and sea conditions. Place recognition and map-based navigation algorithms assume a static world, yet terrain, vessel, and sea-clutter returns share overlapping intensities and morphologies in real radar scans with unreliable separation. Terrain-only radar data are therefore required for training and evaluating maritime autonomy algorithms, but public datasets remain unavailable, and electronic navigational charts (ENCs) cannot reproduce real radar scattering characteristics. We propose m2rDM, a map-conditioned diffusion model with two complementary applications: m2rDM-RS generates synthetic terrain-accurate X-band radar images from ENC map masks without at-sea data collection, and m2rDM-DS suppresses dynamic-object returns in real radar scans to yield terrain-only outputs for downstream perception. The model is conditioned on geolocation-aligned map masks, radar-specific embeddings from a pretrained segmentation encoder, and temporal context from consecutive radar frames. On the MOANA dataset, m2rDM outperforms existing generative baselines in pixel-level accuracy [peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE)] and gray-level co-occurrence matrix (GLCM)-based texture fidelity, with m2rDM-DS achieving the highest reconstruction accuracy and m2rDM-RS producing the closest texture distributions from map masks alone. m2rDM-DS also improves place recognition accuracy on dense-traffic sequences.