MarsTerrNet: A U-Shaped Dual-Backbone Framework with Feature-Guided Loss for Martian Terrain Segmentation

Accurate terrain perception is essential for safe rover operations and reliable geotechnical interpretation of Martian surfaces. The heterogeneous scales, colors, and textures of Martian terrain present significant challenges for semantic segmentation. We present MarsTerrNet, a dual-backbone segmentation framework that combines Progressive Residual Blocks (PRB) with a Swin Transformer to jointly capture fine-grained local details and global contextual dependencies. To further enhance discrimination among geologically correlated classes, we design a feature-guided loss that aligns representative features across terrain categories and reduces confusion between visually similar but physically distinct types. For comprehensive evaluation, we establish MarsTerr2024, an extended dataset derived from the Curiosity rover, providing diverse geological scenes for terrain understanding. Experimental results show that MarsTerrNet achieves state-of-the-art performance and produces geologically consistent segmentation results, supporting automated mapping and geotechnical assessment for future Mars exploration missions.