Spectral-Spatial-Frequency 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
Hyperspectral images (HSIs) are widely used for various Earth observation tasks. However, the complexity of HSIs poses a significant challenge for pixel-wise classification. To effectively extract features from HSIs, deep learning models are extensively utilized to classify HSIs. Although these methods have demonstrated promising performance, they do not consider frequency domain information. To address this problem, a spectral-spatial-frequency transformer (SSFT) network is developed in this paper. The proposed SSFT incorporates a hybrid convolutional block to capture spectral-spatial features, followed by a frequency domain feature extraction block using the discrete Fourier transform. The capability of the designed SSFT is assessed on the University of Trento and University of Houston HSI data. The classification outcomes prove that the SSFT model achieves an overall accuracy of 96.36% and 86.63% respectively, confirming its effectiveness for HSI classification.
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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