A Dual-Branch Local-Global Framework for Cross-Resolution Land Cover Mapping
This addresses the challenge of noisy supervision in weakly supervised land cover mapping for remote sensing applications, though it appears incremental as it builds on existing weakly supervised approaches.
The paper tackles the problem of cross-resolution land cover mapping, where high-resolution predictions must be learned from coarse supervision, by proposing DDTM, a dual-branch weakly supervised framework that decouples local semantic refinement from global contextual reasoning. The result is a new state-of-the-art on the Chesapeake Bay benchmark with 66.52% mIoU, substantially outperforming prior methods.
Cross-resolution land cover mapping aims to produce high-resolution semantic predictions from coarse or low-resolution supervision, yet the severe resolution mismatch makes effective learning highly challenging. Existing weakly supervised approaches often struggle to align fine-grained spatial structures with coarse labels, leading to noisy supervision and degraded mapping accuracy. To tackle this problem, we propose DDTM, a dual-branch weakly supervised framework that explicitly decouples local semantic refinement from global contextual reasoning. Specifically, DDTM introduces a diffusion-based branch to progressively refine fine-scale local semantics under coarse supervision, while a transformer-based branch enforces long-range contextual consistency across large spatial extents. In addition, we design a pseudo-label confidence evaluation module to mitigate noise induced by cross-resolution inconsistencies and to selectively exploit reliable supervisory signals. Extensive experiments demonstrate that DDTM establishes a new state-of-the-art on the Chesapeake Bay benchmark, achieving 66.52\% mIoU and substantially outperforming prior weakly supervised methods. The code is available at https://github.com/gpgpgp123/DDTM.