An evaluation of wavelet-denoised hyperspectral data for remote sensing
Bibliographic record
Abstract
AbstractIn this paper, hyperspectral data cubes that are denoised using a wavelet transform based hybrid spatial–spectral noise-reduction (HSSNR) algorithm are evaluated based on two approaches. The first approach relies on narrow-band vegetation indices and red-edge positions, and the second approach adopts a number of spectral similarity measures. The evaluation shows that the results from the HSSNR denoising algorithm are comparable to those from existing denoising algorithms for vegetation indices and red-edge positions and superior to those from existing denoising algorithms for spectral similarity measures.Dans cet article, des cubes de données hyperspectrales soumises à une procédure de débruitage à l'aide de la transformée en ondelettes basée sur les algorithmes de réduction du bruit HSSNR (« hybrid spatial–spectral noise reduction ») sont évaluées à l'aide de deux approches d'évaluation. La première approche s'appuie sur les indices de végétation en bande étroite et les positions du point d'inflexion du rouge, alors que la seconde approche adopte un certain nombre de mesures de similarité spectrale. Les résultats d'évaluation montrent que l'algorithme de réduction du bruit HSSNR affiche des résultats comparables à ceux des algorithmes de débruitage existants pour les indices de végétation et les positions du point d'inflexion du rouge, et des résultats supérieurs pour les mesures de similarité spectrale. [Traduit par la Rédaction]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".