ST-Mamba: Spatio-Temporal Synergistic Model for Remote Sensing Change Detection
Bibliographic record
Abstract
The advancement of remote sensing and deep learning has spurred interest in high-resolution image change detection (CD). However, pseudo-changes in multi-temporal images, due to complex scenes and variable imaging conditions, often lead to significant misdetection in current methods. To address this problem, we propose a new CD framework: Spatio-Temporal Mamba (ST-Mamba), which consists of three key components. Firstly, a Mamba-based Feature Extraction Module (MFEM) is designed as the encoder to extract essential features from multi-temporal images by leveraging Mamba’s capability to capture inherent information in long data sequences. Secondly, a Spatio-Temporal Synergy Module (STSM) is developed to unify the background features of multi-temporal feature maps into a common domain by employing the state-space model for spatio-temporal modeling. Finally, a Spatio-Temporal Fusion Module (STFM) is created to guide the fusion of image features at different scales and across channels by utilizing a feature map of the unified background features. Experimental results on five widely used change detection datasets show significant improvements over current state-of-the-art methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".