A simple and effective semi-supervised learning framework for hyperspectral image classification
Why this work is in the frame
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Bibliographic record
Abstract
Semisupervised learning (SSL) is often used when the number of labeled samples is very small compared to the number of unlabeled samples. It permits the exploitation of structure within unlabeled samples during the learning task. Like many other applications, remote sensing images suffer from the limited number of ground-truth samples and therefore semisupervised techniques may be used to overcome this limitation. In this paper, a semisupervised framework is proposed for classification of hyperspectral images with scarce labeled samples. Our method, which we call SSL-CC, utilizes fuzzy spectral clustering to label spatially neighboring samples. SSL-CC is implemented and tested on two benchmark hyperspectral datasets. Fuzzy clustering is compared to traditional crisp clustering (k-means) and the obtained results indicate that fuzzy clustering can significantly improve classification accuracy. SSL-CC achieves on average 60% improvement over a baseline SVM classifier.
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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 | 0.001 |
| 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