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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".