Hyperspectral image analysis using a simultaneous Denoising and Intrinsic Order Selection (DIOS) approach
Bibliographic record
Abstract
Recent hyperspectral applications demand for higher accuracy and speed. This thesis develops a hyperspectral application analysis solution to address challenges in the different steps of denoising, order selection and unmixing of hyperspectral application data. Currently, all these steps process the data in cascade to achieve the optimum results. While in existing approaches the desired criterion is different in these steps, the proposed simultaneous Denoising and Intrinsic Order Selection (DIOS) method unifies these criteria. This property not only makes more sense for the desired optimization problem, but also leads to a faster processing algorithm. Consequently, DIOS avoids possible error propagation from the denoising stage to the dimension estimation stage, leading to more accurate results. The proposed method is based on minimizing the estimated Mean Square Error (MSE). The success rate of existing dimension estimation methods declines with the increase of image dimension and the decrease of Signal-to-Noise Ratio (SNR). The most competitive method fails to detect the correct dimension in 30% of cases around 2dB. However, in simulation results DIOS is shown to be successful with a failure rate of about 5%. The proposed unmixing method, based on a simple least square estimation, improves the speed performance least 10 times for an average-sized data cube of 2MB. Compared to some well known existing approaches, the unmixing method improves the estimated MSE up to 60% for SNR<10dB. A new whitening process for hyperspectral applications with coloured noise is also proposed. Since the proposed method avoids the inversion of large matrices, computational complexity is substantially decreased. In the presence of coloured noise, simulation results show that the proposed whitening method lowers the MSE of unmixing and outperforms the existing whitening methods particularly when the noise correction factors increase.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".