A GAN-based Relative Radiometric Correction Model of Remote Sensing Data
Bibliographic record
Abstract
While there are many traditional methods for radiometric correction of multitemporal remote sensing images, deep learning methods are quite rare. Recently it has witnessed a rapid development of computer vision, many style transfer and domain transfer methods have given us great inspiration. However, traditional methods have problem in achieving uniform effect of radiometric correction, while style transfer methods struggle to realize control of local areas and transfer degree, especially when dealing with complicated remote sensing data. Thus, a generative adversarial network (GAN)-based relative radiometric correction method combined with deep style transfer (NormGAN) is proposed. A cycle-consistent structure is applied for bi-directional domain transfer of non-corresponding regions. VGG19 network is used to obtain feature map for the calculation of content loss and style loss. Meanwhile, rough masks of landcover enable transfer within each class and weight matrix is put forward to control transfer degree. Our results indicate that the proposed NormGAN is capable and effective, which has much superiority over other methods.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".