Diverse-Region Hyperspectral Image Classification via Superpixelwise Graph Convolution Technique
Bibliographic record
Abstract
In this paper, a diverse-region hyperspectral image classification (DRHy) method is proposed by considering both irregularly local pixels and globally contextual connections between pixels. Specifically, the proposed method is operated on non-Euclidean graphs, which are constructed by superpixel segmentation methods for diverse regions to cluster irregularly local-region pixels. In addition, the dimensionality reduction method is employed to alleviate the curse of dimensionality problem with a lower computational burden, generating more representative data with the input graph features. In this context, it then constructs a superpixelwise Chebyshev polynomial graph convolution network (ChebyNet) to aggregate global-region superpixels. Benefiting from different superpixel numbers of segmentations, we construct different graph structures, and multiple classification results are obtained, which brings more opportunities to represent the hyperspectral data correctly. Then, all the diverse-region results are further fused by a majority voting technique to improve the final performance. Finally, numerical experiments on two benchmark datasets are provided to demonstrate the superiority of the proposed DRHy-ChebyNet method to the other state-of-the-art methods.
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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 | 0.001 |
| 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 | 0.001 |
| 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 | 0.001 |
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".