Pragmatic Augmentation Algorithms for Deep Learning-Based Cloud and Cloud Shadow Detection in Remote Sensing Imagery
Bibliographic record
Abstract
Identification of clouds and their shadows are two major preprocessing steps for providing an effective interpretation of remotely sensed optical images. Although deep learning-based methods have proved to deliver competent performance for detecting clouds and cloud shadows, improving their generalization ability for reliable results requires a large number of training images and accurate ground truths. As creating ground truth of cloud and cloud shadow is expensive, a practical way to generate more images and their ground truths is to use data augmentation methods. We propose two new data augmentation approaches (one for generating synthetic clouds in scenes and the other one for creating cloud shadows with different levels of shade) so as to achieve natural-looking images with little or no effort required for creating their ground truths. Our experiments show that while each of these approaches is capable of boosting cloud and cloud shadow segmentation individually, the fusion of them improves detecting cloud and shadow in a simultaneous learning pipeline, leading to outperforming the state-of-the-art results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".