{"ID":2868573,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15563","arxiv_id":"2509.15563","title":"DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection","abstract":"Remote sensing change detection (RSCD) is vital for identifying land-cover changes, yet existing methods, including state-of-the-art State Space Models (SSMs), often lack explicit mechanisms to handle geometric misalignments and struggle to distinguish subtle, true changes from noise.To address this, we introduce DC-Mamba, an \"align-then-enhance\" framework built upon the ChangeMamba backbone. It integrates two lightweight, plug-and-play modules: (1) Bi-Temporal Deformable Alignment (BTDA), which explicitly introduces geometric awareness to correct spatial misalignments at the semantic feature level; and (2) a Scale-Sparse Change Amplifier(SSCA), which uses multi-source cues to selectively amplify high-confidence change signals while suppressing noise before the final classification. This synergistic design first establishes geometric consistency with BTDA to reduce pseudo-changes, then leverages SSCA to sharpen boundaries and enhance the visibility of small or subtle targets. Experiments show our method significantly improves performance over the strong ChangeMamba baseline, increasing the F1-score from 0.5730 to 0.5903 and IoU from 0.4015 to 0.4187. The results confirm the effectiveness of our \"align-then-enhance\" strategy, offering a robust and easily deployable solution that transparently addresses both geometric and feature-level challenges in RSCD.","short_abstract":"Remote sensing change detection (RSCD) is vital for identifying land-cover changes, yet existing methods, including state-of-the-art State Space Models (SSMs), often lack explicit mechanisms to handle geometric misalignments and struggle to distinguish subtle, true changes from noise.To address this, we introduce DC-Ma...","url_abs":"https://arxiv.org/abs/2509.15563","url_pdf":"https://arxiv.org/pdf/2509.15563v1","authors":"[\"Min Sun\",\"Fenghui Guo\"]","published":"2025-09-19T03:49:23Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}