Hyperspectral Image Classification With Limited Labeled Training Samples Using Enhanced Ensemble Learning and Conditional Random Fields
Bibliographic record
Abstract
Classification of hyperspectral imagery using few labeled samples is a challenging problem, considering the high dimensionality of hyperspectral imagery. Classifiers trained on limited samples with abundant spectral bands tend to overfit, leading to weak generalization capability. To address this problem, we have developed an enhanced ensemble method called multiclass boosted rotation forest (MBRF), which combines the rotation forest algorithm and a multiclass AdaBoost algorithm. The benefit of this combination can be explained by bias-variance analysis, especially in the situation of inadequate training samples and high dimensionality. Furthermore, MBRF innately produces posterior probabilities inherited from AdaBoost, which are served as the unary potentials of the conditional random field (CRF) model to incorporate spatial context information. Experimental results show that the classification accuracy by MBRF as well as its integration with CRF consistently outperforms the other referenced state-of-the-art classification methods when limited labeled samples are available for training.
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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 | 0.002 | 0.003 |
| 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 | 0.002 |
| 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".