A Variational Approach for Robust Online Fusion of Multiresolution Multispectral Images
Bibliographic record
Abstract
Multi-resolution image fusion is a key problem for real-time satellite imaging, which has a central role in detecting and monitoring the intensity of key natural phenomena such as floods. It aims to solve the trade-off between high temporal and high spatial resolution in remote sensing instruments. Although several algorithms have been proposed to solve this problem, the presence of outliers caused by, e.g., cloud cover downgrades their performance. In this paper, an online image fusion method based on a robust Kalman filter with a weakly supervised approach for temporal dynamics estimation is proposed. Outliers are modelled as a discrete variables, where the probability of contamination for each pixel and spectral band is modelled by a latent variable whose distribution is approximated under variational inference. Experiments fusing images from the MODIS and Landsat 8 sensors show that the proposed approach is significantly more robust against cloud cover, without losing its efficiency when no clouds are present.
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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 | 0.000 |
| 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 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".