SAGE-RSS: Stage-Aligned Global–Local Evidence Refinement for Multi-View Radar Semantic Segmentation

Radar semantic segmentation (RSS) supports robust all-weather perception, yet radar frequency maps exhibit anisotropic structures, weak spectral responses, and temporal dependencies that generic image operators do not address. High-precision RSS must recover sparse target evidence while integrating context across heterogeneous radar projections. We present SAGE-RSS, a stage-aligned global–local evidence refinement framework. A Progressive Support Encoder (PSE) gathers multi-scale support through parallel channel groups and an identity path; a Global–Local Fusion Refiner (GLFR) combines an efficient transform-space branch with residual spatial enhancement; and a Context-Conditioned Axis-aware Decoder (CCAD) performs compact global readout and parallel multi-kernel reconstruction. On CARRADA, SAGE-RSS obtains 63.91% RD mIoU, 46.74% RA mIoU, and 55.33% mean-view mIoU. Under the same profiling protocol, the model runs at 33.30 FPS. The parallel branches and compact context aggregation provide real-time inference and make SAGE-RSS suitable for latency-sensitive and edge-side radar perception applications.