Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement
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Bibliographic record
Abstract
Popular unsupervised change detection algorithms suffer from two problems: first, the difference image generated by bitemporal images usually includes a large number of falsely changed regions due to noise corruption and illumination change; second, fuzzy clustering algorithms are sensitive to noise and they miss the relationship among feature components. To address these issues, we propose a multiscale and multiresolution Gaussian-mixture-model guided by saliency-enhancement (SE-MGMM) for change detection in bitemporal remote sensing images. The proposed SE-MGMM makes two contributions. The first is a novel salient strategy that can enhance saliency objects while suppressing the image background. The strategy uses the saliency weight information to enhance changed regions leading to the improvement of grayscale contrast between changed regions and unchanged regions. The second is that we present a Gaussian-mixture-model based on spatial multiscale and frequency multiresolution information fusion, which can effectively utilize features of difference images and improve detection results of changed regions. Experiments show that the proposed SE-MGMM is robust for both very high-resolution remote sensing images and synthetic aperture radar images. Moreover, the SE-MGMM achieves better change detection and provides better performance metrics than state-of-the-art approaches.
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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