Graph-Transformer with spatial-spectral features fusion for hyperspectral image classification
Bibliographic record
Abstract
Hyperspectral image (HSI) classification plays an important role in interpreting semantics and pixel information. Recently, the graph convolution network (GCN) and vision transformer (ViT) have shown impressive classification capabilities in HSI analysis. Each method offers unique advantages: GCN focuses on local neighborhood features, whereas ViT emphasizes long-range dependencies global features. Existing studies integrated the two methods by serial or parallel for HSI analysis, however, they fell short in deeply fusing the two approaches. To address the challenge, a Graph-Transformer module (GTM) is proposed, which effectively combines local neighborhood features and long-range dependencies global features. Moreover, a spectral feature extraction branch is introduced to enhance spectral learning. Finally, the spatial branch consisting of GTM and spectral branch are fused to complete HSI classification. Experimental results showed that our proposed Graph-Transformer with spatial-spectral features fusion network (GTS 2 F 2 Net) outperformed other state-of-the-art methods on three public datasets. Specifically, it achieved overall accuracy (OA) of 99.31%, 99.69%, and 97.17% on Salinas Valley (SA), Pavia University (PU), Houston 2013, respectively.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".