PID-CD: PID Control is What Change Detection Needs
Bibliographic record
Abstract
Remote Sensing Change Detection (RSCD) is a remote sensing technique used to identify geomorphological changes in bi-temporal images of the same region. However, the identification of change regions is often hindered by false changes in similar objects, resulting in inaccurate boundary recognition of the change regions. This paper adopts the Proportional-Integral-Derivative (PID) control concept and designs two modules: PID Feature Alignment(PIDFA) and PID Feature Fusion (PIDFF), which makes up the PID-CD model. The PIDFA enhances and corrects the change region features using manifold structures and adaptive strategies, while the PIDFF introduces a third branch to guide multi-level feature fusion, progressively refining the boundaries of the change regions. Finally, extensive experiments on benchmark datasets, including LEVIR-CD, S2Looking, and CL-CD, display that PID-CD achieve state-of-the-art performance in RSCD. Specifically, PID-CD achieves the F1 score of 91.94% on LEVIR-CD, 66.61% on S2Looking, and 74.11% on CL-CD, showing significant improvements in detail and boundary of change area.
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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 | 0.000 | 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 | 0.000 | 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".