An Automatic Method for Unsupervised Feature Selection of Hyperspectral Images Based on Fuzzy Clustering of Bands
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Bibliographic record
Abstract
Hyperspectral sensors collect spectral data in numerous adjacent spectral bands which are usually redundant and cause some challenges such as Hughes phenomenon. In this study, an automatic unsupervised method is presented for feature selection from hyperspectral images. To do so, a new statistical feature space is introduced in which each band is regarded as a sample point. This feature space is originated from the statistical attributes of image bands while these attributes are extracted from different partitions of the entire image. A fuzzy clustering of bands, performed in the PCA transformed space of previously mentioned statistical feature space, leads to band clusters with similar characteristics. The proposed band selection technique chooses a representative band in each cluster and removes the other redundant ones. The proposed method is investigated in terms of the classification accuracy of the Pavia University hyperspectral image. Obtained results, which are compared to two recent states of art methods, prove its efficiency.
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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