MARSS: Radar Semantic Segmentation via Modular Attention and State Space Models

Radar semantic segmentation (RSS) is critical for robust perception in adverse conditions, but poses unique challenges: radar frequency maps are highly anisotropic, multiscale, sparse and noisy. Conventional CNN or Transformer architectures, designed for camera images, fail to account for these characteristics, leading degraded performance. We propose MARSS (Modular Attention-enhanced Radar Semantic Segmentation), a novel framework that integrates three specialized modules to address radar-specific issues. In the encoder, the RADE module employs lightweight channel self-attention and depthwise convolutions to robustly encode noisy, anisotropic features. In intermediate layers, the RFAF module performs multi-scale feature fusion and region-level attention to isolate salient radar features. The decoder's RADM module combines state space models with axial self-attention to reconstruct segmentation masks with anisotropy and temporality-aware context. These components collectively suppress noise, disentangle range-Doppler features, and enforce spatial-temporal consistency. On the CARRADA dataset, MARSS achieves substantially higher performance than prior RSS methods, especially for small fast-moving targets.