DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection

• URL: http://arxiv.org/abs/2509.15563v1
• Date: Fri, 19 Sep 2025 03:49:23 GMT
• Title: DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection
• Authors: Min Sun, Fenghui Guo,
• Abstract summary: We introduce DC-Mamba, an "align-then-enhance" framework built upon the ChangeMamba backbone.<n>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.
• Score: 9.305032436286773
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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