{"ID":5937743,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T16:41:18.03494969Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04403","arxiv_id":"2607.04403","title":"MambaRefine-CD: MambaVision with Region-Boundary Temporal Refinement","abstract":"Binary change detection in remote sensing requires both complete changed-region localization and accurate boundary delineation. We present MambaRefine-CD, a region-boundary temporal refinement framework built on a shared MambaVision encoder. The proposed D-RBI module constructs temporal evidence from paired features, absolute differences, and signed differences, then separates it into region and Sobel-conditioned boundary streams. Region features are enhanced with CRAM-lite and decoded by an adaptive receptive-field FPN, while the finest boundary stream guides a bounded residual refinement of the coarse prediction. Experiments on DSIFN-CD and WHU-CD show strong changed-class F1 and IoU under verified evaluation settings, and ablations support the contribution of signed temporal evidence and the full region-boundary refinement pipeline.","short_abstract":"Binary change detection in remote sensing requires both complete changed-region localization and accurate boundary delineation. We present MambaRefine-CD, a region-boundary temporal refinement framework built on a shared MambaVision encoder. The proposed D-RBI module constructs temporal evidence from paired features, a...","url_abs":"https://arxiv.org/abs/2607.04403","url_pdf":"https://arxiv.org/pdf/2607.04403v1","authors":"[\"Dineth Perera\",\"Thaariq Firdous\",\"Oshadha Samarakoon\",\"Roshan Godaliyadda\",\"Parakrama Ekanayake\",\"Vijitha Herath\"]","published":"2026-07-05T16:50:29Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false}