Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features
Bibliographic record
Abstract
Hyperspectral image (HSI) classification is an important topic in remote sensing. In this paper, we improve the principal component analysis (PCA)-based edge preserving features (EPFs) for HSI classification. We select to use minimum noise fraction (MNF) instead of PCA to reduce the dimensionality of the hyperspectral data cube to be classified. We keep all the rest steps from the PCA-based EPFs for HSI classification. Since MNF can preserve fine features of a HSI data cube better than PCA, our new method can outperform PCA-EPFs for HSI classification significantly. Experimental results show that our new method performs better than the PCA-based EPFs under such noisy environment as Gaussian white noise and shot noise. In addition, our MNF+EPFs outperform the PCA+EPFs even when no noise is added to the HSI data cubes for most testing cases, which is very desirable in remote sensing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".