Anomaly detection algorithm based on wavelet decomposition and vertex component analysis in hyperspectral images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to overcome the bad influence caused by complex background in hyperspectral images a new approach for anomaly detection based or wavelet decomposition and vertex component analysis is proposed.Hyperspectral data is decomposed by wavelet decomposition firstly into high frequency images and low frequency image.And then the endmember spectral profile is got from high frequency images by vertex component analysis.At last,anomaly detection is done by spectral angle mapping.The method which needs much less time is proved to be better than the KRX and PCA-KRX algorithms by ROC,and compared with KRX algorithm,the target pixels obtained by the proposed idea are increased by 32.35%,but the false alarm pixels are decreased by 12.12%.
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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 | 0.001 | 0.000 |
| 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 it