Semi-supervised classification of hyperspectral image using random forest algorithm
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a hyperspectral image classification method based on the semi-supervised random forest (SSRF) algorithm. This method uses Deterministic Annealing (DA) and the random forest classifier (RFC). The first step consists of performing the random forest algorithm by using labeled data. Then, image is classified and the probability of each unlabeled data will be computed. Based on the probability and the temperature parameter, label of unlabeled data will be determined. Finally, the classification is carried out based on the labeled data and unlabeled data which were converted to labeled data in the procedure of algorithm. The proposed method and also a conventional RFC method have been applied to an APEX (Airborne Prism Experiment) hyperspectral image. The results show more consistency in homogeneous area. In addition, its overall accuracy of classification is 82.63%, while the kappa coefficient is 0.78, and both are higher than the accuracies of spectral based classification using the conventional RFC, i.e. 73.58% and 0.68 respectively.
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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 | 0.000 | 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