Gan-based SAR to Optical Image Translation in Fire-Disturbed Regions
Bibliographic record
Abstract
Climate change by anthropogenic warming leads to increases in dry fuels and promotes forest fires. Multispectral images' quality is easily affected by poor atmospheric conditions. SAR satellite sensors can penetrate through clouds and image day and night. However, the burned area mapping methods widely used for optical data are not feasible to be applied for SAR data owing to the differences in imaging mechanisms. Recent advances in deep image translation can fill this gap by using Generative Adversarial Networks (GAN). In this research, we apply a GAN-based model for SAR to optical image translation over fire-disturbed regions. Specifically, Sentinel-1 SAR images are translated into Sentinel-2 images using the ResNet-based Pix2Pix model, which is trained on 281 large fire events and tested on the other 23 events in Canada. The generated images preserve the spectral characteristics well and show high similarity to the real images with Structure Similarity Index Measure (SSIM) over 0.59.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".