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

arXiv: https://arxiv.org/abs/2509.15563 · HTML: https://mlsanity.com/paper/2509.15563/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 9 of 9)

- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [Changer: Feature Interaction is What You Need for Change Detection](https://mlsanity.com/paper/2209.08290/) (2022)
- [Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2011.09766/) (2020)
- [Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2108.07002/) (2021)
- [Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images](https://mlsanity.com/paper/1812.00645/) (2018)

## Abstract (arXiv)

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.