Change Detection Approach for SAR Imagery Based on Arc-Tangential Difference Image and <i>k</i> -Means++
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Bibliographic record
Abstract
In this letter, an unsupervised change detection (CD) approach based on arc-tangential difference and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -Means++ clustering is presented for synthetic aperture radar (SAR) remote-sensing images. The images are first standardized with their variance values using a logarithmic function applied to multitemporal images. The difference image (DI) is then calculated by subtracting the SAR images using the arc-tangential subtraction operator. After that, the DI is subjected to a 2-D Gaussian filter and a median filter, respectively. Filters are essential for determining the best feature space for CD. The 2-D Gaussian filter smooths DIs to retain local area consistency, while the median filter handles edge information. Finally, using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -Means++, a quick and efficient clustering approach, filtered data is clustered into two classes. Experiments using real-world datasets in Bern, Ottawa, and Yellow River have demonstrated that the given technique is fast, successful, and effective.
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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