Sea Ice Classification Using PCA Based Image Denoising on GNSS-R Delay-Doppler Maps
Bibliographic record
Abstract
This study proposes a principal component analysis (PCA) based denoising scheme for global navigation satellite system reflectometry (GNSS-R) delay Doppler map (DDM) based sea ice classification. GNSS-R DDM data is obtained from the TechDemoSat-1 (TDS-1) satellite, and sea ice type ground truth is derived from National Snow and Ice Data Center (NSIDC) sea ice concentration data. DDMs were denoised by first computing PCA on a TDS-1 satellite track, then conducting the forward and inverse PCA transformations on each DDM to retain only the DDM pixels with the greatest variance. DDM denoising was conducted prior to the calculation of standard classification features such as DDM average (DDMA). The denoised classification observables were provided to an extremely randomized trees (ET) machine learning classifier. The final classification accuracy was 88.32% with a 0.7533 Kappa coefficient. F1 scores were 92.52%, 75.57%, and 83.30% for first-year ice (FYI), multi-year ice (MYI), and thin ice (TI), respectively. The proposed method resulted in improvements across all performance metrics when compared against classification without PCA based observables and denoising. Most significant increases were observed in MYI precision, TI precision, and FYI recall, which were improved by 22.72%, 4.89%, and 6.19%, respectively.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".