A change detection measure based on a likelihood ratio and statistical properties of SAR intensity images
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Bibliographic record
Abstract
Abstract With its weather- and illumination-independent characteristics, synthetic aperture radar (SAR) has become an important tool for change detection. There are two critical steps in SAR image change detection: designing a change detector and choosing a decision rule. Given a measure from a change detector, the change detection results could be sensitive to the decision rule, such as the selection of a threshold. This letter presents a change detection measure based on a likelihood ratio and the statistical distribution of SAR intensity images. The likelihood ratio is defined as the ratio between the joint probability density functions (PDFs) of a pair of SAR images. Under the condition that both PDFs follow the gamma distribution, the histogram of this change detection measure deduced from the likelihood ratio has a single and steep peak that can be used to reliably and easily determine the change detection threshold. Analyses of SAR image pairs from different platforms show that the proposed change detection measure is simple and effective in detecting changes. Acknowledgements The authors would like to thank the Chinese Scholarship Council for providing funds for this study. The authors are also thankful to Dr. Timothy Warner and the anonymous reviewers for their detailed comments, which helped very much to improve this letter.
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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 | 0.000 |
| 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 it