Noise estimation in a noise-adjusted principal component transformation and hyperspectral image restoration
Bibliographic record
Abstract
We apply a noise-adjusted principal component transformation (NAPCT) to an Earth Observing 1 (EO-1) Hyperion image whose noise structure is typically unknown. In this paper, we propose to simulate and estimate the noise covariance structure of either a body of water, such as an ocean or lake, or a horizontal piece-wise delineation along a spatially homogeneous area. The effect is compared to that of the near-neighbor difference method utilized in some of the literature. A strategy is proposed of efficiently and accurately locating the noisy bands, particularly the striping bands and the striping columns. It automates the task of manual examination of each band and is particularly useful for hyperspectral data. We illustrate algorithmically that the implementation of NAPCT can be achieved by application of the procedure in linear discriminant analysis (LDA). The resultant images of NAPCT are compared to those from standard principal component transformation (PCT). By using the first 10 NAPCT bands (almost striping and noise free), which explain 99.8% of total data variability, we can reproject the NAPCT image back onto the original spectral space for visualization and image enhancement. The quality of the restored hyperspectral image is greatly improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".