A Dual Frequency Transformer Network for Hyperspectral Image Classification
Bibliographic record
Abstract
Hyperspectral images (HSIs) provide detailed spectral information of objects to be detected and play an important role in distinguishing targets with a similar appearance. However, the characteristics of high dimensionality and complexity impose significant challenges for realizing pixel-wise classification. Although existing convolutional neural networks (CNNs) and transformer-based models have presented promising performance for HSIs classification, they mainly extract features from spectral-spatial perspective and do not fully consider the information in the frequency domain. To address this issue, in this paper, we reconsider feature extraction and HSIs classification from the frequency domain. Specifically, inspired by the observation that high-frequency information contains detailed features within a local receptive field whereas low-frequency information provides global smooth variations, a frequency domain feature extraction (FDFE) block with dual branches is developed. In the FDFE block, an multi-head neighborhood attention (MSNA) block and a global filter block are designed to capture high- and low-frequency features, respectively. Besides, a pixel embedding module is constructed. Based on these, a novel hierarchical dual frequency transformer network (DFTN) is developed. Extensive experiments are performed on three open public hyperspectral datasets to evaluate the performance of our developed method. The experimental results demonstrate that our method is efficient and robust for HSIs classification, achieving overall accuracies of 94.14%, 86.92%, and 96.72% on the University of Pavia, University of Houston, and University of Trento datasets, 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".