Automatic Cloud Detection and Removal in Satellite Imagery Using Deep Learning Techniques
Bibliographic record
Abstract
With the rapid advancement of remote sensing technology, satellite imagery has become increasingly vital in global geographic information systems, environmental monitoring, and resource management.However, cloud cover frequently degrades the quality of satellite images, limiting their effectiveness in many critical areas.Traditional methods for cloud detection and removal, such as threshold analysis and spectral feature analysis, often fail to achieve satisfactory results due to environmental constraints and algorithmic limitations.In response, this study employs deep learning techniques, specifically superpixel segmentation and generative adversarial networks (GAN), to address this issue.This paper begins by discussing the importance of cloud detection and removal in satellite imagery and reviews existing major techniques and methods.It then explores the application of superpixel segmentation based on local adaptive distance for automatic cloud boundary identification, along with innovative applications of GAN for surface information reconstruction in cloudcovered areas.These methods not only enhance the accuracy of cloud detection but also effectively optimize the cloud removal process, paving the way for further applications of satellite imagery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".