A Comparative Analysis of Deep Neural Networks (DNN) for Recognition of Tropical Cyclones in Real-Time Satellite Images over the Indian Sub-Continent
Bibliographic record
Abstract
Supervised learning is typically required to train a Deep Neural Network (DNN) to identify satellite cyclone images with noise and blur in the visible and infrared spectrum.This requires input-target pairs of noisy images and corresponding blurry photos.In this research, we propose a self-supervised learning method to train a Deep Neural Network (DNN) employing only real-time images from the visible and infrared spectrums.The suggested technique, which serves as a self-supervision tool, can identify convective activity, the eye of the storm, and wall clouds in the tropical cyclone cloud distribution.Our approach involves two stages: Offline pre-training on Cyclonic Storm (CS) images over the Indian sub-continent, North Indian Ocean, Arabian Sea, and Bay of Bengal was followed by real-time testing of the localization on INSAT-3D satellite images.This allows for efficient testing of the model.Satellite cyclone images of recent tropical cyclones from 2018 to 2023 are used to assess the algorithm's efficacy thoroughly.An analysis of performance metrics is attempted with graphical plots and a precision and recall matrix.Furthermore, according to the experimental results, our suggested algorithm outperforms the state-of-the-art models in terms of both classification accuracy and localization learning models' test-time performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".