Light-Weighted Explainable Dual Transformer Network for Hyperspectral Image Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although light-weighted explainable deep learning techniques are critical for operational hyperspectral image (HSI) classification, it is very challenging to achieve these techniques due to difficulties to deal with the spatial-spectral complexity and coupling effect in HSI. Leveraging the excellent feature learning capability of the attention mechanism, this paper presents a spatial-spectral dual transformer (SSDT) network that decomposes the conventional spatial-spectral transformer operation into a spatial transformer and a spectral transformer, which not only reduce the model complexity, but also allows the use of self-attention to explain feature relevance. The proposed approach is tested on some benchmark HSI scenes and the results demonstrate that the proposed dual transformer network not only achieves new state-of-the-art performance due to its excellent feature extraction capability, but also enables the analysis and visualization of feature importance and decision making process.
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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