SAR Image Change Detection Method Based on Neural-CRF Structure
Bibliographic record
Abstract
There are two problems in SAR image change detection when using difference images (DIs), i.e., 1) the subtraction operation results in serious loss of semantic information in DIs; and 2) the boundary of DI is uncertain. We propose a change detection method based on Neural-CRF structure. Firstly, Transformer-UNet (TR-UNet) is designed to provide the unary potential for CRF. The TR-Attention module improves the semantic expression ability of UNet by introducing the multi-head attention mechanism of TR. Secondly, a cascade CRF as Recurrent Neural Network, named as C-CRF-RNN, is proposed to update the unary potential and pairwise potential simultaneously. This improves the ability of CRF-RNN to refine pixel-level label prediction. Experiments show that the proposed method consistently outperforms the state-of-the-art methods on two benchmarks including berne data and ottawa data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".