A Review and Analysis of GAN-Based Super-Resolution Approaches for INSAT 3D/3DR Satellite Imagery using Artificial Intelligence
Bibliographic record
Abstract
The Indian National Satellite System (INSAT)-3D/3DR is a geostationary satellite that is used for meteorological applications in the Indian region. Geostationary satellites have significant spatial coverage and good temporal resolution that help to monitor the evolution and propagation of meteorological systems. Meteorologists use satellite images to observe the locations of severe weather and understand the physical processes involved in the system. Image Super-Resolution (SR) aims to convert low-resolution images into high-resolution images while maintaining image quality. The SR techniques will improve the visualization of convective systems and tropical cyclones, facilitating accurate location-based warnings. This paper presents a comparative comparison of computer models for converting Low-Resolution (LR)(INSAT)-3D/3DR images into super-resolution images. This study also discusses and investigates the various Generative Adversarial Network (GAN)-based models, including the Super Resolution Generative Adversarial Network (SRGAN), Enhanced Super Resolution Generative Adversarial Network (ESRGAN), and Real Enhanced Super Resolution Generative Adversarial Network (Real-ESRGAN). The findings are compared to established approaches such as Bicubic Interpolation and Super-Resolution Convolution Neural Network (SRCNN). This study demonstrates that Real-ESRGAN performs better on weather satellite images than other cutting-edge approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".