Graph-Transformer with spatial-spectral features fusion for hyperspectral image classification
Why this work is in the frame
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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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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 | 0.001 |
| 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